Proceedings of the Twelfth Language Resources and Evaluation Conference
Neural Mention Detection (2020.lrec-1)
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| Challenge: | Mention detection is an important preprocessing step for downstream applications such as NER and coreference resolution. |
| Approach: | They propose and compare three approaches to mention detection using ELMO embeddings and a biaffine classifier. |
| Outcome: | The proposed model outperforms state-of-the-art models on the GENIA corpora and improves on mention recall. |
A Cluster Ranking Model for Full Anaphora Resolution (2020.lrec-1)
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| Challenge: | Anaphora resolution systems designed for CONLL 2012 dataset can handle key aspects of the full anaphora task such as the identification of singletons and of certain types of non-referring expressions. |
| Approach: | They propose an architecture to identify non-referring expressions and build coreference chains, including singletons, using system mentions. |
| Outcome: | The proposed model performs better on the CONLL 2012 dataset than the state-of-the-art system. |
Mandarinograd: A Chinese Collection of Winograd Schemas (2020.lrec-1)
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| Challenge: | Mandarinograd is a corpus of Winograd Schemas in Mandarin Chinese . WS are hard to collect and few datasets are publicly available . |
| Approach: | They introduce a corpus of Winograd Schemas in Mandarin Chinese . they describe the difficulties faced when building the corpus and explain how they overcome the anomalies. |
| Outcome: | The proposed corpus of Winograd Schemas in Mandarin Chinese is hard to build and resistant to statistical methods. |
On the Influence of Coreference Resolution on Word Embeddings in Lexical-semantic Evaluation Tasks (2020.lrec-1)
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| Challenge: | Existing word embeddings rely on local information delimited by context windows or dependency parents to predict word relations. |
| Approach: | They propose to use coreference resolution to find all spans of a text that refer to the same entity to improve the F1-Scores. |
| Outcome: | The proposed methods do not benefit significantly from pronoun substitution. |
NoEl: An Annotated Corpus for Noun Ellipsis in English (2020.lrec-1)
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| Challenge: | Ellipsis resolution is an important step to improve the accuracy of mainstream natural language processing tasks such as information retrieval, event extraction, dialog systems, etc. |
| Approach: | They extend the study of ellipsis by annotating a corpus for noun ellippsis and closely related phenomenon using the first hundred movies of Cornell Movie Dialogs Dataset. |
| Outcome: | The proposed corpus has 946 instances of exophoric and endophorical noun ellipsis, making it the biggest resource of nouns in English, to the best of our knowledge. |
An Annotated Dataset of Coreference in English Literature (2020.lrec-1)
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| Challenge: | Using OntoNotes, coreference resolution systems are typically evaluated on this data exclusively. |
| Approach: | They present a new dataset of coreference annotations for works of literature in English covering 29,103 mentions in 210,532 tokens from 100 works of fiction published between 1719 and 1922. |
| Outcome: | The proposed dataset covers 29,103 mentions in 210,532 tokens from 100 works of fiction published between 1719 and 1922. |
GerDraCor-Coref: A Coreference Corpus for Dramatic Texts in German (2020.lrec-1)
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| Challenge: | Dramatic texts are highly structured literary text types with linguistic and literary properties. |
| Approach: | They present an annotated corpus of German dramatic texts and preliminary experiments on automatic coreference resolution. |
| Outcome: | The proposed system achieves a 28.8 CoNLL score in dramatic texts compared to other dialogical text types such as interviews . the proposed system is expected to be extended to include the (partial) information given in the dramatis personae . |
A Study on Entity Resolution for Email Conversations (2020.lrec-1)
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| Challenge: | This paper addresses the task of entity resolution in email conversations. |
| Approach: | They propose to create an annotated seed corpus of email threads labeled with entity coreference chains and evaluate their models for the task. |
| Outcome: | The proposed model performs well on the entity resolution task for email conversations. |
Model-based Annotation of Coreference (2020.lrec-1)
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| Challenge: | Annotators are asked to annotate coreferent spans of text, which is unnatural . we present an alternative in which annotators can preprocess documents and assign pronouns to entities. |
| Approach: | They propose an alternative in which annotators are asked to assign pronouns to entities and preprocess documents to create a knowledge base. |
| Outcome: | The proposed model-based approach leads to faster annotation and higher inter-annotator agreement and opens up an alternative approach to coreference resolution. |
French Coreference for Spoken and Written Language (2020.lrec-1)
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| Challenge: | In French, most coreference resolution systems run different setups, making comparisons difficult. |
| Approach: | They present a full-stack model that outperforms other approaches for coreference resolution in French . they compare it with the first end-to-end neural French coreference model trained on democrat . |
| Outcome: | The proposed model outperforms the current systems for spoken and written French. |
Cross-lingual Zero Pronoun Resolution (2020.lrec-1)
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| Challenge: | In pronoun-dropping languages, predicate arguments are not realized instead of being realized as overt pronounos. |
| Approach: | They propose a BERT-based model for zero pronoun resolution in Arabic and Chinese . they also evaluate BERT feature extraction and fine-tune models on the task . |
| Outcome: | The proposed model outperforms the state-of-the-art model for Arabic and Chinese on OntoNotes 5.0. |
Exploiting Cross-Lingual Hints to Discover Event Pronouns (2020.lrec-1)
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| Challenge: | Non-nominal co-reference is much less studied than nominal coreference because of the lack of annotated corpora. |
| Approach: | They propose to use parallel multilingual corpora to create artificially labeled data for the classification of three different readings of the English pronoun ‘it’: entity, event or pleonastic. |
| Outcome: | The proposed method can be used to classify three different readings of the English pronoun ‘it’ from their translation in several languages. |
MuDoCo: Corpus for Multidomain Coreference Resolution and Referring Expression Generation (2020.lrec-1)
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| Challenge: | a new dataset, MuDoCo, is composed of authored dialogs between a fictional user and a system . the dialogs cross domains and users exhibit complex task switching behavior . |
| Approach: | They propose a new dataset, MuDoCo, composed of authored dialogs between a fictional user and a system . they propose two baseline models for the downstream tasks: coreference resolution and referring expression generation. |
| Outcome: | The proposed dataset contains 8,429 dialogs with an average of 5.36 turns per dialog . the users exhibit complex task switching behavior such as re-initiating a previous task . |
Affection Driven Neural Networks for Sentiment Analysis (2020.lrec-1)
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| Challenge: | Existing deep neural network models lack mechanisms to highlight important sentiment terms. |
| Approach: | They propose a method to incorporate affective knowledge into deep neural network models by mapping affective influence vectors to an affective impact value and integrating them into long-term memory models to highlight affective terms. |
| Outcome: | The proposed approach improves on three large datasets by 1.0% to 1.5% on the benchmark datasets. |
The Alice Datasets: fMRI & EEG Observations of Natural Language Comprehension (2020.lrec-1)
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| Challenge: | "naturalistic" stimuli are now offering a new way to study language comprehension in the brain, in synergy with natural language processing tools. |
| Approach: | They propose to use a set of datasets from a story in English to test new linguistic and computational hypotheses about natural language comprehension in the brain. |
| Outcome: | The Alice Datasets are a set of datasets based on magnetic resonance and electrophysiological data, collected while participants heard a story in English. |
Modelling Narrative Elements in a Short Story: A Study on Annotation Schemes and Guidelines (2020.lrec-1)
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| Challenge: | Text-processing algorithms that annotate main components of a story are in great need of corpora and well-agreed annotation schemes. |
| Approach: | They propose a model that generalizes a narrative structure in the form of world building elements (characters, time and space) and text worlds themselves and switches between them. |
| Outcome: | The proposed model can be used for annotating narratives in corpora of literary texts, criminal evidence, teaching materials, quests, etc. |
Cortical Speech Databases For Deciphering the Articulatory Code (2020.lrec-1)
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| Challenge: | The articulatory code (AC) is a neural code exchanging multi-item messages between the short-term memory and cortical areas as the vSMC and STG. |
| Approach: | They propose to validate the AC-hypotheses by measuring the activity of all ensembles of neurons coding and decoding the AC. |
| Outcome: | The proposed hypothesis is based on neuroscience, phonetics and the science of evolution and is compared with current methods. |
ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation (2020.lrec-1)
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| Challenge: | a new dataset of eye-tracking and electroencephalography captures language understanding . eye movement data provides millisecond-accurate records of where humans look when reading . |
| Approach: | They recorded and preprocessed eye-tracking and electroencephalography data during natural reading and during annotation. |
| Outcome: | The study combines eye-tracking and electroencephalography to capture the reading process . the data can be used to evaluate state-of-the-art machine learning systems . |
Linguistic, Kinematic and Gaze Information in Task Descriptions: The LKG-Corpus (2020.lrec-1)
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| Challenge: | linguistic structure of utterances referring to concrete actions may reflect the structure of the sensorimotor processing underlying the same action. |
| Approach: | They present a dataset that integrates linguistic, kinematic and gaze data with an explicit focus on relations between action and language. |
| Outcome: | The proposed dataset integrates linguistic, kinematic and gaze data with an explicit focus on relations between action and language. |
The ACQDIV Corpus Database and Aggregation Pipeline (2020.lrec-1)
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| Challenge: | ACQDIV corpus database and aggregation pipeline aims to identify universal cognitive processes that allow children to acquire any language. |
| Approach: | They present the ACQDIV corpus database and aggregation pipeline . the tool aims to identify universal cognitive processes that allow children to acquire any language . |
| Outcome: | The ACQDIV corpus database and aggregation pipeline is a tool developed by the European Research Council . the database represents 15 corpora from 14 typologically maximally diverse languages . |
Providing Semantic Knowledge to a Set of Pictograms for People with Disabilities: a Set of Links between WordNet and Arasaac: Arasaac-WN (2020.lrec-1)
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Didier Schwab, Pauline Trial, Céline Vaschalde, Loïc Vial, Emmanuelle Esperanca-Rodier, Benjamin Lecouteux
| Challenge: | Pictograms are a tool that is increasingly used by people with cognitive or communication disabilities. |
| Approach: | They propose a database that links WordNet and Arasaac to link pictograms to semantic knowledge. |
| Outcome: | The proposed database links pictograms with WordNet and Arasaac to create language-independent prototypes. |
Orthographic Codes and the Neighborhood Effect: Lessons from Information Theory (2020.lrec-1)
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| Challenge: | a recent study has shown that orthography is a core issue in contemporary word reading models. |
| Approach: | They consider the orthographic neighborhood effect as an important control variable in psycholinguistic studies of word reading. |
| Outcome: | The results show that weighting features improves the orthographic neighborhood effect significantly across languages. |
Understanding the Dynamics of Second Language Writing through Keystroke Logging and Complexity Contours (2020.lrec-1)
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| Challenge: | a large corpus of data on keystroke logging is available for research on literacy (reading and writing) this resource is a reflection of the urgent need to obtain ecologically valid data . |
| Approach: | They propose to use Etherpad's keystroke logging data to analyze complex texts . they propose to relate behavioral data to indices of syntactic and lexical complexity . |
| Outcome: | The proposed method aims to improve alignment between keystroke-logging measures and cognitive processes and L2 writing performance measures. |
Design of BCCWJ-EEG: Balanced Corpus with Human Electroencephalography (2020.lrec-1)
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| Challenge: | Recent research has focused on the fusion of NLP and neuroscience of language. |
| Approach: | They propose to use a balanced corpus of written Japanese (BCCWJ) annotated with human electroencephalography to improve annotations and annotations. |
| Outcome: | The proposed language resource is annotated with human electroencephalography (EEG) and can improve on annotations, genres, languages, etc. |
Using the RUPEX Multichannel Corpus in a Pilot fMRI Study on Speech Disfluencies (2020.lrec-1)
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Katerina Smirnova, Nikolay Korotaev, Yana Panikratova, Irina Lebedeva, Ekaterina Pechenkova, Olga Fedorova
| Challenge: | Numerous classifications of disfluencies have been proposed and/or implemented in annotating speech corpora. |
| Approach: | They propose to use Russian multichannel corpus RUPEX to create fragments of speech disfluencies and their clusters. |
| Outcome: | The proposed method allows to create fragments in terms of requirements for the fMRI BOLD temporal resolution. |
Construction of an Evaluation Corpus for Grammatical Error Correction for Learners of Japanese as a Second Language (2020.lrec-1)
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| Challenge: | The Lang-8 corpus is suitable as a training dataset for machine translation-based grammatical error correction systems but it is not suitable as an evaluation dataset because corrected sentences sometimes include inappropriate sentences. |
| Approach: | They created an evaluation corpus for correcting grammatical errors made by Japanese as a second language learners using neural machine translation and statistical machine translation techniques. |
| Outcome: | The proposed corpus has less noise and its annotation scheme reflects the characteristics of the dataset, making it ideal for correcting grammatical errors in sentences written by learners of Japanese as a Second Language (JSL). |
Effective Crowdsourcing of Multiple Tasks for Comprehensive Knowledge Extraction (2020.lrec-1)
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Sangha Nam, Minho Lee, Donghwan Kim, Kijong Han, Kuntae Kim, Sooji Yoon, Eun-kyung Kim, Key-Sun Choi
| Challenge: | Existing studies on information extraction from unstructured texts lack a coherent evaluation of all tasks. |
| Approach: | They propose to use crowdsourcing data to develop a Korean information extraction initiative point . they propose to train and evaluate four Korean information extracting tasks using a state-of-the-art model . |
| Outcome: | The proposed model will be used to evaluate four Korean information extraction tasks using crowdsourcing data. |
Developing a Corpus of Indirect Speech Act Schemas (2020.lrec-1)
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| Challenge: | Indirect speech acts (ISAs) involve utterances whose literal meanings are not identical to their intended meanings. |
| Approach: | They propose a formal representation of ISA Schemas required for such testing, including a measure of the difficulty of a particular schema. |
| Outcome: | The proposed model minimizes the amount of expert authoring needed and maximizes realism. |
Quality Estimation for Partially Subjective Classification Tasks via Crowdsourcing (2020.lrec-1)
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| Challenge: | a common approach to quality estimation is to ask multiple reviewers to evaluate the same artifacts. |
| Approach: | They propose a probabilistic model for subjective classification tasks that incorporates the qualities of artifacts as well as the abilities and biases of creators and reviewers as latent variables to be jointly inferred. |
| Outcome: | The proposed model estimates the quality of speech more effectively than a vote aggregation, measured by correlation with a fine-grained classification by experts. |
Crowdsourcing in the Development of a Multilingual FrameNet: A Case Study of Korean FrameNet (2020.lrec-1)
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| Challenge: | Using current methods, the construction of multilingual FrameNets is expensive and complex. |
| Approach: | They evaluated whether crowdsourcing approaches captured cross-cultural and cross-linguistic meanings . they found that crowd workers made intuitive choices comparable to trained FrameNet experts . |
| Outcome: | The results are now available in Korean FrameNet 1.1. |
Towards a Reliable and Robust Methodology for Crowd-Based Subjective Quality Assessment of Query-Based Extractive Text Summarization (2020.lrec-1)
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| Challenge: | a growing number of documents are needed for multi-document summarization. |
| Approach: | They propose crowdsourcing to evaluate intrinsic and extrinsic quality of extractive text summaries . they conduct intensive comparative crowdsourcing and laboratory experiments . |
| Outcome: | The proposed crowdsourcing task evaluates intrinsic and extrinsic quality of extractive text summaries. |
A Seed Corpus of Hindu Temples in India (2020.lrec-1)
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| Challenge: | a scientific study of temples can reveal valuable insights into culture and heritage of India. |
| Approach: | They propose a platform that creates temple corpus from web text on temples. |
| Outcome: | The proposed platform improves the curation of temple corpus using classifiers trained on Wikipedia articles on Hindu temples. |
Do You Believe It Happened? Assessing Chinese Readers’ Veridicality Judgments (2020.lrec-1)
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| Challenge: | Using data from news datasets, we examine readers' veridicality judgments to news events at sentence level. |
| Approach: | They collect and study Chinese readers’ veridicality judgments to news events . goal is to observe pragmatic behaviors of linguistic features under context . |
| Outcome: | The aim is to observe the pragmatic behaviors of linguistic features under context which affects readers in making veridicality judgments. |
Creating Expert Knowledge by Relying on Language Learners: a Generic Approach for Mass-Producing Language Resources by Combining Implicit Crowdsourcing and Language Learning (2020.lrec-1)
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Lionel Nicolas, Verena Lyding, Claudia Borg, Corina Forascu, Karën Fort, Katerina Zdravkova, Iztok Kosem, Jaka Čibej, Špela Arhar Holdt, Alice Millour, Alexander König, Christos Rodosthenous, Federico Sangati, Umair ul Hassan, Anisia Katinskaia, Anabela Barreiro, Lavinia Aparaschivei, Yaakov HaCohen-Kerner
| Challenge: | Lack of wide-coverage and high-quality LRs is a longstanding issue in natural language processing (NLP) however, there are no large initiatives of similar scale for creating new LR or improving existing ones. |
| Approach: | They propose a generic approach to combine implicit crowdsourcing and language learning to mass-produce language resources (LRs) they describe its core paradigm that consists in pairing specific types of LRs with specific exercises . |
| Outcome: | The proposed approach can be used in several learning scenarios to produce a multitude of NLP resources and alleviate the long-standing issue of the lack of LRs. |
MAGPIE: A Large Corpus of Potentially Idiomatic Expressions (2020.lrec-1)
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| Challenge: | Existing corpora cover less than 5,000 instances of less than 100 different idiom types . large corpus allows for better evaluation of assumptions about idiomatic expressions . |
| Approach: | They propose to build the largest-to-date corpus of idioms for English using crowdsourcing methods. |
| Outcome: | The proposed corpus is larger than existing resources and contains rich metadata and is made publicly available. |
CRWIZ: A Framework for Crowdsourcing Real-Time Wizard-of-Oz Dialogues (2020.lrec-1)
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| Challenge: | Crowdsourcing platforms such as Amazon Mechanical Turk have been effective for collecting large corpora of task-based and open-domain conversational dialogues, but difficulties arise when task- based dialogues require expert domain knowledge or rapid access to domain-relevant information. |
| Approach: | They propose a framework for collecting real-time Wizard of Oz dialogues through crowdsourcing for collaborative, complex tasks. |
| Outcome: | The proposed framework avoids interactions that breach procedures only known to experts while enabling the capture of a wide variety of interactions. |
Effort Estimation in Named Entity Tagging Tasks (2020.lrec-1)
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| Challenge: | Named Entity Recognition (NER) requires large amounts of annotated data. |
| Approach: | They investigate which variables influence the time spent on a named entity annotation task by a human . they found a root mean squared error (RMSE) of 25.68 words per minute with a Nearest Neighbors model . |
| Outcome: | The proposed model achieves a root mean squared error (RMSE) of 25.68 words per minute with a Nearest Neighbors model. |
Using Crowdsourced Exercises for Vocabulary Training to Expand ConceptNet (2020.lrec-1)
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Christos Rodosthenous, Verena Lyding, Federico Sangati, Alexander König, Umair ul Hassan, Lionel Nicolas, Jolita Horbacauskiene, Anisia Katinskaia, Lavinia Aparaschivei
| Challenge: | Language resources (LRs) are expensive to create and maintain, and this makes it difficult to create or extend LRs. |
| Approach: | They propose to use a Telegram chatbot interface to gather knowledge on word relations suitable for expanding ConceptNet with new words. |
| Outcome: | The proposed model allows to gather 12,000 answers from learners on different question types over 16 days and shows that it is a potential tool for crowdsourcing and fostering vocabulary skills. |
Predicting Multidimensional Subjective Ratings of Children’ Readings from the Speech Signals for the Automatic Assessment of Fluency (2020.lrec-1)
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| Challenge: | Using a novel framework, we estimate the reading performance of young readers using linguistic and phonetic features. |
| Approach: | They propose a framework for performing such an estimation that exploits multiple references performed by adults and demonstrate its efficiency using recordings of 273 pupils. |
| Outcome: | The proposed framework exploits multiple references performed by adults and shows that it is efficient. |
Constructing Multimodal Language Learner Texts Using LARA: Experiences with Nine Languages (2020.lrec-1)
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Elham Akhlaghi, Branislav Bédi, Fatih Bektaş, Harald Berthelsen, Matthias Butterweck, Cathy Chua, Catia Cucchiarin, Gülşen Eryiğit, Johanna Gerlach, Hanieh Habibi, Neasa Ní Chiaráin, Manny Rayner, Steinþór Steingrímsson, Helmer Strik
| Challenge: | LARA is an open source project that aims to support easy conversion of plain texts into online versions suitable for use by language learners. |
| Approach: | They propose to support easy conversion of plain texts into online versions suitable for use by language learners. |
| Outcome: | The proposed platform is suitable for creating texts in multiple languages via crowdsourcing techniques that can be used for teaching a language via reading and listening. |
A Dataset for Investigating the Impact of Feedback on Student Revision Outcome (2020.lrec-1)
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| Challenge: | Despite numerous studies on the kinds of feedback that can best promote learning, this question remains an open debate in the area of Second Language Acquisition (SLA). |
| Approach: | They annotate a corpus of student-written sentences with teacher feedback provided for the errors. |
| Outcome: | The proposed annotation scheme and the teacher feedback dataset are based on student-written sentences in their original and revised versions with teacher feedback provided for the errors. |
Creating Corpora for Research in Feedback Comment Generation (2020.lrec-1)
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| Challenge: | Existing corpus of learner corpora with feedback comments is limited due to the lack of public access to this task. |
| Approach: | They describe two corpora that have been manually annotated with feedback comments . they describe how the principle and guidelines for feedback comment annotation work . |
| Outcome: | The proposed corpus is available on the web and will facilitate research in feedback comment generation. |
Using Multilingual Resources to Evaluate CEFRLex for Learner Applications (2020.lrec-1)
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| Challenge: | The Common European Framework of Reference for Languages defines six levels of learner proficiency and links them to particular communicative abilities. |
| Approach: | They propose to compile lexical resources that link single words and multi-word expressions to specific CEFR levels. |
| Outcome: | The results show that the English CEFRLex resource is in accordance with external resources that are gold standard. |
Immersive Language Exploration with Object Recognition and Augmented Reality (2020.lrec-1)
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Benny Platte, Anett Platte, Christian Roschke, Rico Thomanek, Thony Rolletschke, Frank Zimmer, Marc Ritter
| Challenge: | Using Augmented Reality (AR) in teaching and learning contexts for language is still young. |
| Approach: | They propose to use Augmented Reality to explore language in different languages by using a deep learning method. |
| Outcome: | The proposed application recognizes up to 1000 objects in the user’s environment and names them accordingly. |
A Process-oriented Dataset of Revisions during Writing (2020.lrec-1)
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| Challenge: | Revisions are defined as "changes at any point in the writing process" a dataset of 7,120 revisions was created to analyze revisions in writing . |
| Approach: | They use keystroke data and eye tracking data of 65 students to analyze revisions . they define revisions as "changes at any point in the writing process" |
| Outcome: | The proposed dataset includes 7,120 revisions from 65 students from different backgrounds . each type of revision can have a different effect on the written product or writing quality . |
Automated Writing Support Using Deep Linguistic Parsers (2020.lrec-1)
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Luís Morgado da Costa, Roger V P Winder, Shu Yun Li, Benedict Christopher Lin Tzer Liang, Joseph Mackinnon, Francis Bond
| Challenge: | Automated Grammar Error Detection (GED) and Grammar Erreor Correction (GEC) are tasks that have attracted some attention within the NLP community. |
| Approach: | They propose a web-based system that integrates English Grammatical Error Detection (GED) and course-specific stylistic guidelines to automatically review and provide feedback on student assignments. |
| Outcome: | The system integrates both general NLP methods and high precision parsers to check student assignments before they are submitted for grading. |
TLT-school: a Corpus of Non Native Children Speech (2020.lrec-1)
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| Challenge: | a corpus of speech utterances collected in schools of northern italy is being used to assess the performance of students learning both English and German. |
| Approach: | a corpus of speech utterances collected in schools of northern italy is described . the corpus is going to be freely distributed to scientific community . |
| Outcome: | The corpus of speech utterances collected in schools of northern italy is a "Trentino Language Testing" in schools" the data are used to assess the performance of students learning English and German . |
Toward a Paradigm Shift in Collection of Learner Corpora (2020.lrec-1)
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| Challenge: | a pilot version of the Revita Learner Corpus (ReLCo) is available for Russian learners . it is collected and annotated automatically while learners practice with Revita . |
| Approach: | They present the first version of the longitudinal Revita Learner Corpus (ReLCo) for Russian . the corpus contains 8 422 sentences exhibiting several types of errors committed by learners . |
| Outcome: | The Russian version of the Revita Learner Corpus is publicly available . the pilot study shows that the corpus grows continuously while learners practice . |
Quality Focused Approach to a Learner Corpus Development (2020.lrec-1)
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| Challenge: | Existing methods for annotating learner corpus with errors are not well defined and can be repetitive. |
| Approach: | The paper proposes a quality focused approach to a learner corpus development . the approach includes comparison of digitized texts, text correction, automated morphological analysis and manual review of annotations. |
| Outcome: | The proposed method is used to create a learner corpus in Latvian . it reduces the amount of mistakes that could be introduced due to inconsistent correction or carelessness. |
An Exploratory Study into Automated Précis Grading (2020.lrec-1)
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| Challenge: | Existing systems for writing evaluation focus on argumentative texts . a précis is a written text that provides a coherent summary of main points . |
| Approach: | They propose to use a corpus of English précis texts to train a machine learning model . they find it is able to predict the grade of précis texts with only a moderate error margin . |
| Outcome: | The proposed model predicts the grade of précis texts with only a moderate error margin. |
Adjusting Image Attributes of Localized Regions with Low-level Dialogue (2020.lrec-1)
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| Challenge: | Image editing is time-consuming and requires a wide assortment of features and combinations of these features to achieve a desired effect. |
| Approach: | They propose a task-oriented dialogue system to investigate low-level instructions for NLIE . 25% of users found the system easy-to-use, resonating with their motivation . |
| Outcome: | The proposed system is easy-to-use and user-friendly. |
Alignment Annotation for Clinic Visit Dialogue to Clinical Note Sentence Language Generation (2020.lrec-1)
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| Challenge: | Despite advances in natural language processing, converting a clinic visit conversation into a clinical note is a largely unexplored area of research. |
| Approach: | They propose an annotation methodology that is content- and technique- agnostic while associating note sentences to sets of dialogue sentences. |
| Outcome: | The proposed method is content- and technique-agnostic while associating note sentences to sets of dialogue sentences. |
MultiWOZ 2.1: A Consolidated Multi-Domain Dialogue Dataset with State Corrections and State Tracking Baselines (2020.lrec-1)
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Mihail Eric, Rahul Goel, Shachi Paul, Abhishek Sethi, Sanchit Agarwal, Shuyang Gao, Adarsh Kumar, Anuj Goyal, Peter Ku, Dilek Hakkani-Tur
| Challenge: | MultiWOZ 2.0 has substantial noise in dialogue state annotations and dialogue utterances . follow-up work has augmented the original dataset with user dialogue acts . |
| Approach: | They propose to reannotate dialogue state and utterances based on original dataset . they then compare their results to other datasets to improve their models . |
| Outcome: | The proposed dataset improves on the noise in the dialogue state annotations and dialogue utterances. |
A Comparison of Explicit and Implicit Proactive Dialogue Strategies for Conversational Recommendation (2020.lrec-1)
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| Challenge: | Existing literature on traditional and conversational recommendation systems, but how to provide suggestions is still an open question. |
| Approach: | They propose to use explicit and implicit strategies to compare user preferences and a proactive strategy to provide information from a gathered system to determine user acceptance. |
| Outcome: | The proposed strategies significantly influence the perception of human-computer interaction. |
Conversational Question Answering in Low Resource Scenarios: A Dataset and Case Study for Basque (2020.lrec-1)
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| Challenge: | Existing datasets for conversational question answering systems are expensive and limited in resources . a dataset of thousands of dialogues and tens of thousands question answering turns is available for free . |
| Approach: | They aim to test the performance of Conversational Question Answering systems in non-English languages . they use a dataset built on top of Wikipedia sections about popular people and organizations . |
| Outcome: | The results show that the system can handle low-resource conditions comparable to English . the results also show that dialogue history models are not directly transferable to another language . |
Construction and Analysis of a Multimodal Chat-talk Corpus for Dialog Systems Considering Interpersonal Closeness (2020.lrec-1)
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| Challenge: | a large-scale multimodal dialog corpus is needed to accelerate research on dialog systems that can handle social signals and verbal information. |
| Approach: | They construct a multimodal dialog corpus focusing on the relationship between speakers and 19 pairs of participants. |
| Outcome: | The proposed system is based on a multimodal dialog corpus of 19,303 utterances (10 hours) from 19 pairs of participants. |
BLISS: An Agent for Collecting Spoken Dialogue Data about Health and Well-being (2020.lrec-1)
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Jelte van Waterschoot, Iris Hendrickx, Arif Khan, Esther Klabbers, Marcel de Korte, Helmer Strik, Catia Cucchiarini, Mariët Theune
| Challenge: | Structured interviews are a time-consuming and inefficient way to gather information about people's well-being. |
| Approach: | They propose to build an artificial intelligence agent which asks questions about happiness . they build a prototype of the agent and collect 55 spoken dialogues . |
| Outcome: | The proposed agent collects 55 spoken dialogues and asks users about happiness and well-being. |
The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service (2020.lrec-1)
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| Challenge: | Existing datasets for human-like dialogue tasks are deficient due to the complexity of human conversations. |
| Approach: | They construct a large-scale Chinese E-commerce conversation corpus with 1 million dialogues, 20 million utterances, and 150 million words. |
| Outcome: | The proposed dataset includes 1 million multi-turn dialogues, 20 million utterances, and 150 million words. |
“Cheese!”: a Corpus of Face-to-face French Interactions. A Case Study for Analyzing Smiling and Conversational Humor (2020.lrec-1)
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| Challenge: | Cheese! is a conversational corpus containing 11 mixed and non-mixed dyadic interactions lasting around 15 minutes each. |
| Approach: | They propose to use a conversational corpus to compare smiling behavior in American English and French conversations to conduct a cross-cultural comparison. |
| Outcome: | The proposed study examines the relationship between smile and humor in conversational interactions between American English and French participants. |
The Margarita Dialogue Corpus: A Data Set for Time-Offset Interactions and Unstructured Dialogue Systems (2020.lrec-1)
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| Challenge: | Time-Offset Interaction Applications (TOIAs) simulate face-to-face conversations between humans and digital human avatars recorded in the past. |
| Approach: | They propose a methodology for creating the knowledge base for a TOIA, a dialogue corpus, and baselines for single-turn answer retrieval. |
| Outcome: | The proposed method lets the avatar maker list pairs by intuition, guessing what possible questions a user may ask to the avatar. |
How Users React to Proactive Voice Assistant Behavior While Driving (2020.lrec-1)
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| Challenge: | Nowadays Personal Assistants (PAs) are available in multiple environments and become increasingly popular to use via voice. |
| Approach: | They conducted a usability study in which 42 participants perceive proactive voice output in a Wizard-of-Oz study in . traffic density was varied during a highway drive and it included six in-car-specific use cases. |
| Outcome: | The proposed suggestions should not be obtrusive nor increase drivers’ cognitive load, while enhancing user experience. |
Emotional Speech Corpus for Persuasive Dialogue System (2020.lrec-1)
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| Challenge: | Emotional expressions can be used to express the speaker’s emotion more directly than using only emotion expression in the text. |
| Approach: | They built a speech dialogue corpus in a persuasive scenario that uses emotional expressions to build a system with emotional expression. |
| Outcome: | The proposed system can express the speaker's emotion more directly than using only emotion expression in the text, and the results show that the collected emotional expressions with their speeches have higher emotional expressiveness for expressing the system's emotions to users. |
Multimodal Analysis of Cohesion in Multi-party Interactions (2020.lrec-1)
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Reshmashree Bangalore Kantharaju, Caroline Langlet, Mukesh Barange, Chloé Clavel, Catherine Pelachaud
| Challenge: | Existing studies have associated group cohesion with performance, team satisfaction and adherence. |
| Approach: | They propose to use 16 two-minute segments annotated with group cohesion from the AMI corpus to analyze their impact on perceived level of cohesicity. |
| Outcome: | The proposed analysis shows that laughter and interruption are higher in high cohesive segments. |
Treating Dialogue Quality Evaluation as an Anomaly Detection Problem (2020.lrec-1)
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| Challenge: | Dialogue systems for interaction with humans are becoming more popular . the best way to estimate their success is through means of human evaluation . |
| Approach: | They investigate the effectiveness of perceiving dialogue evaluation as an anomaly detection task. |
| Outcome: | The proposed approach is based on four models and shows negative results . the proposed approach could be used in the future to improve human-led dialogue evaluations. |
Evaluation of Argument Search Approaches in the Context of Argumentative Dialogue Systems (2020.lrec-1)
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| Challenge: | Argumentative dialogue systems and chat bots require a database of arguments that matches their requirements. |
| Approach: | They propose a dialogue system that presents arguments by virtual avatar and synthetic speech to users and allows them to rate the presented content in four different categories. |
| Outcome: | The proposed system evaluates arguments retrieved by two state-of-the-art argument search engines and a system based on traditional web search. |
PATE: A Corpus of Temporal Expressions for the In-car Voice Assistant Domain (2020.lrec-1)
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| Challenge: | In the voice assistant domain, temporal expression recognition is a key module for AI voice assistants, but research on temporal recognition has focused on data from the news, the clinical domain, and social media. |
| Approach: | They propose a crowdsourcing method for eliciting natural-language commands containing temporal expressions for an AI voice assistant by using pictures and scenario descriptions. |
| Outcome: | The proposed method elicits natural-language commands containing temporal expressions using pictures and scenario descriptions. |
Mapping the Dialog Act Annotations of the LEGO Corpus into ISO 24617-2 Communicative Functions (2020.lrec-1)
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| Challenge: | ISO 24617-2 is the ISO standard for dialog act annotation. |
| Approach: | They map the original dialog act labels of the LEGO corpus into the communicative functions of ISO 24617-2 . they propose to use this data to develop approaches for dialog act recognition . |
| Outcome: | The mapped dialogs improve performance while recognizing communicative functions . the standard is based on 17 English dialogs, which are used in the study . |
Estimating User Communication Styles for Spoken Dialogue Systems (2020.lrec-1)
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| Challenge: | a neural network estimation system for spoken dialogues can be used to estimate the communication style of a user's interaction, but this is rarely implemented in a live system. |
| Approach: | They propose a neural network approach to estimate the communication style of spoken interaction, namely elaborateness and directness. |
| Outcome: | The proposed method can estimate the elaborateness and directness of spoken interaction and improve the results with additional linguistic features. |
The ISO Standard for Dialogue Act Annotation, Second Edition (2020.lrec-1)
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Harry Bunt, Volha Petukhova, Emer Gilmartin, Catherine Pelachaud, Alex Fang, Simon Keizer, Laurent Prévot
| Challenge: | ISO standard 24617-2 for dialogue act annotation has been used in corpus annotation and in the design of components for spoken and multimodal interactive systems. |
| Approach: | ISO standard 24617-2 for dialogue act annotation is proposed for a second edition . this second edition allows a more accurate annotation of dependence relations and rhetorical relations in dialogue. |
| Outcome: | The proposed second edition of ISO 24617-2 for dialogue act annotation addresses some inaccuracies and undesirable limitations. |
The AICO Multimodal Corpus – Data Collection and Preliminary Analyses (2020.lrec-1)
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| Challenge: | Existing studies on human multimodal behaviour in interactions with a human or a robot partner are limited. |
| Approach: | They describe the first explorative research on the AICO Multimodal Corpus, which contains eye-gaze, Kinect, and video recordings of human-robot and human-human interactions. |
| Outcome: | The AICO Multimodal Corpus contains eye-gaze, Kinect, and video recordings of human-robot and human-human interactions. |
A Corpus of Controlled Opinionated and Knowledgeable Movie Discussions for Training Neural Conversation Models (2020.lrec-1)
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| Challenge: | Fully data driven Chatbots suffer from inconsistent behaviour across their turns due to a general difficulty in controlling parameters like their assumed background personality and knowledge of facts. |
| Approach: | They propose a model that is based on pre-specified facts and opinions and validates the dialogues for adherence to their given fact and opinion profile. |
| Outcome: | The proposed model is able to generate opinionated responses that are judged to be natural and knowledgeable and show attentiveness. |
A French Medical Conversations Corpus Annotated for a Virtual Patient Dialogue System (2020.lrec-1)
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| Challenge: | Existing methods for creating virtual patient dialogue systems require large data specific to the language, domain and clinical cases studied. |
| Approach: | They propose to build an annotated corpus of medical dialogues in french using medical interviews and a data annotation scheme. |
| Outcome: | The proposed corpus is made publicly available under a Free/Libre Open Source licence. |
Getting To Know You: User Attribute Extraction from Dialogues (2020.lrec-1)
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| Challenge: | a new method to extract user attributes from dialogues is needed to improve user understanding. |
| Approach: | They propose to leverage dialogues with conversational agents to automatically extract user attributes from dialogues. |
| Outcome: | The proposed model surpasses retrieval and generation baselines on human evaluation. |
Augmenting Small Data to Classify Contextualized Dialogue Acts for Exploratory Visualization (2020.lrec-1)
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| Challenge: | a new corpus of conversations is being developed to support data visualization exploration . we use data augmentation to improve our methods for dialogue act classification . |
| Approach: | They propose to use a corpus of conversations to annotate contextualized dialogue acts . they highlight how thinking aloud affects interpretation of dialogue acts in the context . |
| Outcome: | The proposed AI can support visualization exploration with a small corpus of conversations . the proposed AI outperforms existing models in terms of performance and performance . |
RDG-Map: A Multimodal Corpus of Pedagogical Human-Agent Spoken Interactions. (2020.lrec-1)
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| Challenge: | a corpus of 209 spoken game dialogues between a human and a remote-controlled artificial agent is presented. |
| Approach: | They present a multimodal corpus of 209 spoken game dialogues between a human and a remote-controlled artificial agent. |
| Outcome: | The proposed corpus consists of 209 spoken game dialogues between a human and a remote-controlled artificial agent. |
MPDD: A Multi-Party Dialogue Dataset for Analysis of Emotions and Interpersonal Relationships (2020.lrec-1)
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| Challenge: | Existing datasets with emotion and relation labels for dialogues are limited. |
| Approach: | They use a Chinese dialogue dataset to annotate emotions and interpersonal relationships on each utterance. |
| Outcome: | The proposed dataset contains 25,548 utterances from 4,142 dialogues. |
“Alexa in the wild” – Collecting Unconstrained Conversations with a Modern Voice Assistant in a Public Environment (2020.lrec-1)
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| Challenge: | Currently, many studies on human-machine interactions focus on private usage, short pre-defined tasks or specific domains. |
| Approach: | They propose to collect 40 hours of device directed utterances during a science exhibition in germany and extract transcripts of both visitors requests and Alexa answers. |
| Outcome: | The proposed dataset provides an unconstrained, unscripted public interaction with a voice assistant during a science exhibition in germany. |
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators (2020.lrec-1)
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| Challenge: | Emotion recognition helps to build natural dialogue systems. |
| Approach: | They propose to use a recurrent neural model to annotate emotion corpora with dialogue act labels and an ensemble annotator to extract the final dialogue act label. |
| Outcome: | The proposed model annotates two accessible multi-modal emotion corpora with and without context and extracts the final dialogue act label. |
PACO: a Corpus to Analyze the Impact of Common Ground in Spontaneous Face-to-Face Interaction (2020.lrec-1)
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| Challenge: | PAC0 is a conversational corpus of 15 face-to-face interactions lasting around 20 min each. |
| Approach: | They have created a conversational corpus of 15 face-to-face dyadic interactions lasting around 20 min each. |
| Outcome: | The compared corpus consists of 15 face-to-face dyadic interactions lasting around 20 min each. |
Dialogue Act Annotation in a Multimodal Corpus of First Encounter Dialogues (2020.lrec-1)
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| Challenge: | a method used to annotate dialogue acts in a multimodal corpus is described . the annotations allow for analysis of how multimodal signals contribute to the structure and content of the dialogues. |
| Approach: | They propose to annotate dialogue acts in a multimodal corpus of first encounter dialogues . they focus on which dialogue acts often follow each other across speakers and which overlap gestural behaviour . |
| Outcome: | The method used to annotate dialogue acts in a multimodal corpus is described. |
A Conversation-Analytic Annotation of Turn-Taking Behavior in Japanese Multi-Party Conversation and its Preliminary Analysis (2020.lrec-1)
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| Challenge: | a new conversation-analytic annotation scheme is proposed for multi-party conversations . current systems do not take a turn like a human even in simple two-party conversation . |
| Approach: | They propose a conversation-analytic annotation scheme for turn-taking behavior in multi-party conversations . they analyze how syntactic and prosodic features of utterances vary across four selection types . |
| Outcome: | The proposed model is based on Japanese multi-party conversations. |
Understanding User Utterances in a Dialog System for Caregiving (2020.lrec-1)
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| Challenge: | a dialog system that can monitor the health status of seniors has a huge potential for solving the labor shortage in the caregiving industry in aging societies. |
| Approach: | They are developing a yes/no response classifier and an entailment recognizer to correctly interpret user utterances. |
| Outcome: | The proposed system can correctly interpret user utterances and can monitor the health of seniors. |
Designing Multilingual Interactive Agents using Small Dialogue Corpora (2020.lrec-1)
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| Challenge: | a new study aims to develop a design framework for multilingual interactive agents . large amounts of data and language resources are needed to develop most key components . |
| Approach: | They propose a general design framework for multilingual interactive agents in specialized domains with small or non-existent dialogue corpora. |
| Outcome: | The proposed framework integrates external language services for supporting multilingual functions and realizes context-aware dialogue generation under the situation of small corpora. |
Multimodal Corpus of Bidirectional Conversation of Human-human and Human-robot Interaction during fMRI Scanning (2020.lrec-1)
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Birgit Rauchbauer, Youssef Hmamouche, Brigitte Bigi, Laurent Prévot, Magalie Ochs, Thierry Chaminade
| Challenge: | a study of real-life bi-directional conversations combines multimodal corpus with neural, physiological and behavioral data. |
| Approach: | They propose a multimodal corpus derived from natural conversations . they used human-human interactions as a control condition . |
| Outcome: | The proposed corpus includes neural, physiological and behavioral data. |
The Brain-IHM Dataset: a New Resource for Studying the Brain Basis of Human-Human and Human-Machine Conversations (2020.lrec-1)
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| Challenge: | Using a dataset of controlled interactions, we have studied the feedback items produced by the interlocutors during a conversation. |
| Approach: | They propose to use a dataset of controlled interactions to study feedback items and a virtual reality context to re-synthesize the conversations. |
| Outcome: | The proposed dataset compares human-human and human-machine production of feedbacks and is the first of its kind. |
Dialogue-AMR: Abstract Meaning Representation for Dialogue (2020.lrec-1)
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Claire Bonial, Lucia Donatelli, Mitchell Abrams, Stephanie M. Lukin, Stephen Tratz, Matthew Marge, Ron Artstein, David Traum, Clare Voss
| Challenge: | Abstract Meaning Representation (AMR) does not capture the illocutionary force or speaker’s intended contribution in the broader dialogue context. |
| Approach: | They propose a schema that enriches Abstract Meaning Representation (AMR) it provides a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems. |
| Outcome: | The proposed schema provides a semantic representation for facilitating Natural Language Understanding (NLU) in human-robot dialogue systems. |
Relation between Degree of Empathy for Narrative Speech and Type of Responsive Utterance in Attentive Listening (2020.lrec-1)
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| Challenge: | In order for a spoken dialogue agent to be recognized as a listener of narratives, it is necessary to generate responsive utterances. |
| Approach: | They propose to classify responsive utterances based on degree of empathy . quantitative evaluations of 37,995 responsive . utterations were performed using a simulated robot listening to a human narrative. |
| Outcome: | The proposed classification based on empathy shows that responsive utterances show empathy to narratives and enhance speaker's motivation to speak. |
Intent Recognition in Doctor-Patient Interviews (2020.lrec-1)
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| Challenge: | Currently, up to 20 percent of patients are misdiagnosed in medical training programs. |
| Approach: | They propose to annotate doctor-patient interviews with intent inventory and information retrieval methods that are robust with respect to small amounts of training data. |
| Outcome: | The proposed models provide baseline performance scores on the data set for further research. |
BrainPredict: a Tool for Predicting and Visualising Local Brain Activity (2020.lrec-1)
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| Challenge: | Using fMRI, we recorded a corpus of human-human and human-robot conversations while participants brain activity was recorded with f.MRI, but we did not find any tools for displaying together brain activity prediction of non-controlled conversations, the raw material used in this prediction and the features used for these predictions. |
| Approach: | They propose a tool that allows dynamic prediction and visualization of an individual’s local brain activity during a conversation using raw behavioral data. |
| Outcome: | The proposed tool takes as input behavioral features computed from raw data, mainly the participant and the interlocutor speech but also the participant’s visual input and eye movements. |
MTSI-BERT: A Session-aware Knowledge-based Conversational Agent (2020.lrec-1)
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| Challenge: | Several models have been published achieving promising results in all the major linguistic tasks. |
| Approach: | They propose to exploit a BERT-based model to handle multi-turn conversations . they propose to use PuffBot to monitor asthma patients . |
| Outcome: | The proposed model can handle multi-turn conversations, a type of conversations that differs from single-turn by the presence of multiple related interactions. |
Predicting Ratings of Real Dialogue Participants from Artificial Data and Ratings of Human Dialogue Observers (2020.lrec-1)
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| Challenge: | Using linear regression, we developed dialogue evaluation functions based on features from the WOz dialogues and the MTurkers’ ratings, and the dialogues on the woz participants’ ratings. |
| Approach: | They collected a corpus of dialogues in a Wizard of Oz (WOz) setting in the Internet of Things domain and asked users to rate the system on intelligence, naturalness, personality, friendliness, their enjoyment, overall quality, and whether they would recommend the system to others. |
| Outcome: | The proposed evaluation functions can predict three conversational aspects for the Wizard of Oz (WOz) setting. |
Which Model Should We Use for a Real-World Conversational Dialogue System? a Cross-Language Relevance Model or a Deep Neural Net? (2020.lrec-1)
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| Challenge: | End-to-end neural network models of conversational dialogue are popular for conversational tasks, but there are still questions about how well they work for real applications and how much data is needed to achieve acceptable performance. |
| Approach: | They compare two different kinds of end-to-end dialogue models based on cross-language relevance and cross-linguistic LSTM models for corpus-based selection of dialogue responses. |
| Outcome: | The proposed models perform well on a large corpus, but are dominated by a more moderate-sized corpus. |
Chinese Whispers: A Multimodal Dataset for Embodied Language Grounding (2020.lrec-1)
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| Challenge: | In this paper, we introduce a multimodal dataset in which subjects are instructing each other how to assemble IKEA furniture. |
| Approach: | They propose a multimodal dataset in which subjects are instructing each other how to assemble IKEA furniture. |
| Outcome: | The proposed method avoids implicit experimenter biases by allowing subjects to instruct each other on the nature of the task: the process of the furniture assembly. |
AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue (2020.lrec-1)
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| Challenge: | Current architectures only take care of semantic and contextual information for a given query and fail to fully account for syntactic and external knowledge which are crucial for generating responses in a chit-chat system. |
| Approach: | They propose a multi-stream deep learning architecture that learns unified embeddings for query-response pairs by incorporating Graph Convolution Networks over their dependency parse. |
| Outcome: | The proposed architecture improves on the next sentence prediction task and significantly improves existing techniques. |
An Annotation Approach for Social and Referential Gaze in Dialogue (2020.lrec-1)
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| Challenge: | Existing studies on eye gaze information focus on social functions and how it is used in reference resolution. |
| Approach: | They propose an approach for annotating eye gaze considering its social and referential functions in multi-modal dialogue. |
| Outcome: | The proposed annotation scheme is based on eye gaze behavior cues in human-human dialogues. |
A Penn-style Treebank of Middle Low German (2020.lrec-1)
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| Challenge: | attestation for Middle Low German is rich, but its syntax remains relatively understudied. |
| Approach: | They outline the issues involved in creating a Penn-style treebank of Middle Low German . they describe the background for the corpus and the process by which texts were selected . |
| Outcome: | The proposed corpus will be a syntactically annotated treebank of Middle Low German . the proposed corpuse will be part of the Corpus of Historical Low German (CHLG) the proposed method will be used to generate strong empirical evidence for the language . |
Books of Hours. the First Liturgical Data Set for Text Segmentation. (2020.lrec-1)
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Amir Hazem, Beatrice Daille, Christopher Kermorvant, Dominique Stutzmann, Marie-Laurence Bonhomme, Martin Maarand, Mélodie Boillet
| Challenge: | Until now, the book of hours has been scarcely studied because of its manuscript nature, its length and its complex content. |
| Approach: | They propose to use Handwritten Text Recognition to generate a corpus of Latin transcriptions of 300 books of hours generated by OCR for handwritten and not printed texts. |
| Outcome: | The proposed structure and state-of-the-art methods are compared with existing methods and are based on the results of a systematic evaluation of two books of hours. |
Corpus of Chinese Dynastic Histories: Gender Analysis over Two Millennia (2020.lrec-1)
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| Challenge: | Chinese dynastic histories form a long continuous linguistic space of approximately 2000 years . there is no freely available corpus of these histories, making Classical Chinese low-resource. |
| Approach: | a project introduces an open-source corpus of twenty-four dynastic histories covered by a Creative Commons license. |
| Outcome: | a new open-source corpus of 20 dynastic histories is developed for analysis of historical linguistic use of male and female terms. |
The Royal Society Corpus 6.0: Providing 300+ Years of Scientific Writing for Humanistic Study (2020.lrec-1)
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| Challenge: | a new version of the Royal Society Corpus covers 300+ years of scientific writing . the corpus is freely available under a Creative Commons license, excluding copy-righted parts . |
| Approach: | They present a new version of the Royal Society Corpus, a diachronic corpus of scientific English covering 300+ years of scientific writing. |
| Outcome: | The extended version of the Royal Society Corpus covers 300+ years of scientific writing . the corpus is freely available under a Creative Commons license, excluding copy-righted parts . |
Corpus REDEWIEDERGABE (2020.lrec-1)
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| Challenge: | The corpus REDEWIEDERGABE contains detailed annotations for speech, thought and writing representation (ST&WR) with approximately 490,000 tokens, it is the largest resource of its kind. |
| Approach: | This paper presents corpus REDEWIEDERGABE, a German-language historical corpus with detailed annotations for speech, thought and writing representation (ST&WR). |
| Outcome: | The corpus REDEWIEDERGABE contains 490,000 tokens and is the largest resource of its kind. |
WeDH - a Friendly Tool for Building Literary Corpora Enriched with Encyclopedic Metadata (2020.lrec-1)
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| Challenge: | Linked Open Data repositories are difficult to use for text corpora enriched with metadata . a collaborative project aims to fill the access to textual resources available on the web and the possibility of combining these resources with sources of metadata extending the life and maintenance of the data itself. |
| Approach: | They propose a web interface that allows users to leverage encyclopedic knowledge from DBpedia, wikidata and VIAF to enrich texts with bibliographical and exegetical knowledge. |
| Outcome: | WeDH aims to fill the access to textual resources available on the web and the possibility of combining these resources with sources of metadata that can enrich the texts with useful information. |
Automatic Section Recognition in Obituaries (2020.lrec-1)
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| Challenge: | Obituaries contain information about people’s values across times and cultures, which makes them useful for exploring cultural history. |
| Approach: | They propose to use a convolutional neural network to recognize these sections in obituaries to improve their annotation. |
| Outcome: | The proposed model outperforms bag-of-words and embedding-based BiLSTMs and BiLStm-CRFs with a micro F1 = 0.81. |
SLäNDa: An Annotated Corpus of Narrative and Dialogue in Swedish Literary Fiction (2020.lrec-1)
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| Challenge: | a new corpus of literary fiction has been annotated for cited materials with a focus on dialogue. |
| Approach: | They propose to annotate a new corpus of Swedish literary fiction for cited materials with a focus on dialogue. |
| Outcome: | The proposed corpus can be used to train and analyze models for different types of analysis of literary narrative and speech. |
RiQuA: A Corpus of Rich Quotation Annotation for English Literary Text (2020.lrec-1)
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| Challenge: | In literature, spoken interactions between characters are of central importance to the narrative. |
| Approach: | They propose to annotate quotations, including their interpersonal structure, for English literary text. |
| Outcome: | The proposed dataset provides a rich view of dialogue structures not available from other available corpora. |
A Corpus Linguistic Perspective on Contemporary German Pop Lyrics with the Multi-Layer Annotated “Songkorpus” (2020.lrec-1)
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| Challenge: | TEI-compliant song lyrics are used as primary data, linguistically and literary motivated annotations, and extralinguistic metadata. |
| Approach: | They propose to annotate a multiply annotated corpus of German lyrics as a publicly available basis for multidisciplinary research. |
| Outcome: | The proposed corpus of german lyrics is available for evaluation and analysis using TEI-compliant, linguistically and literary motivated annotations and extralinguistic metadata. |
The BDCamões Collection of Portuguese Literary Documents: a Research Resource for Digital Humanities and Language Technology (2020.lrec-1)
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| Challenge: | a new corpus of literary documents in Portuguese is presented . it includes close to 4 million words from over 200 complete documents . the corpus is suitable for research in language technology and digital humanities . |
| Approach: | They present the BDCames Collection of Portuguese Literary Documents, a new corpus of literary texts written in Portuguese. |
| Outcome: | The BDCames Collection of Portuguese Literary Documents is a new corpus of literary documents written in Portuguese . it includes close to 4 million words from over 200 complete documents from 83 authors in 14 genres . the corpus is suitable for research in language technology and language science and digital humanities . |
Dataset for Temporal Analysis of English-French Cognates (2020.lrec-1)
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| Challenge: | Using computational techniques to study language evolution has gained much attention . comparing two or more languages can shed light on how they co-evolve . |
| Approach: | They propose to use a dataset to investigate the similarity in evolution between languages by comparing cognates across time. |
| Outcome: | The proposed dataset is the first to use computational approaches and large data to make a cross-language diachronic analysis. |
Material Philology Meets Digital Onomastic Lexicography: The NordiCon Database of Medieval Nordic Personal Names in Continental Sources (2020.lrec-1)
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| Challenge: | Sprkbanken Text is an infrastructure containing modern and historical written data. |
| Approach: | They propose to use a database to contain medieval Nordic personal names attested in Continental sources. |
| Outcome: | The proposed database combines formally interlinked onomastic data with digitized versions of the medieval manuscripts from which the data originate and information on the tokens’ context. |
NLP Scholar: A Dataset for Examining the State of NLP Research (2020.lrec-1)
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| Challenge: | Google Scholar is the largest web search engine for academic literature and provides access to rich metadata associated with the papers. |
| Approach: | They extracted citation information from the ACL Anthology (AA) for about 44 thousand NLP papers and identified authors who published at least three papers there. |
| Outcome: | The ACL Anthology (AA) is the largest repository of articles on Natural Language Processing (NLP). |
The DReaM Corpus: A Multilingual Annotated Corpus of Grammars for the World’s Languages (2020.lrec-1)
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| Challenge: | Until recently, language descriptions were available in paper form only, with indexes as the only search aid. |
| Approach: | They propose to digitize a multilingual corpus of language descriptions and annotate it with various meta, word, and text attributes to make searching and analysis easier and more useful. |
| Outcome: | The proposed corpus is searchable through a couple of well-established corpus infrastructures. |
LiViTo: Linguistic and Visual Features Tool for Assisted Analysis of Historic Manuscripts (2020.lrec-1)
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| Challenge: | a mixed methods approach is feasible for the identification of scribes and authors in handwritten documents. |
| Approach: | They propose a mixed methods approach to the identification of scribes and authors in handwritten documents . they use a software tool which combines linguistic insights and computer vision techniques . |
| Outcome: | The proposed tool can be used to identify scribes and authors in handwritten documents. |
TextAnnotator: A UIMA Based Tool for the Simultaneous and Collaborative Annotation of Texts (2020.lrec-1)
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| Challenge: | Existing annotation tools are not efficient for the annotation of corpora and are not error-free. |
| Approach: | They propose to extend existing annotation tools by evaluating their flexibility and efficiency. |
| Outcome: | The proposed system performs platform-independent multimodal annotations and annotates complex textual structures. |
Deduplication of Scholarly Documents using Locality Sensitive Hashing and Word Embeddings (2020.lrec-1)
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| Challenge: | Deduplication is the task of identifying near and exact duplicate data items in a collection. |
| Approach: | They propose a method which uses locality sensitive hashing and meaning representation to determine (near) duplicates. |
| Outcome: | The proposed method achieves a macro F1-score of 0.90 on a ground truth dataset of 100K scholarly documents. |
“Voices of the Great War”: A Richly Annotated Corpus of Italian Texts on the First World War (2020.lrec-1)
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Federico Boschetti, Irene De Felice, Stefano Dei Rossi, Felice Dell’Orletta, Michele Di Giorgio, Martina Miliani, Lucia C. Passaro, Angelica Puddu, Giulia Venturi, Nicola Labanca, Alessandro Lenci, Simonetta Montemagni
| Challenge: | “Voices of the Great War” is the first large corpus of Italian historical texts dating back to the period of First World War. |
| Approach: | "Voices of the Great War" is the first large corpus of Italian historical texts dating back to the period of First World War. |
| Outcome: | The "Voices of the Great War" corpus is the first large corpus of Italian historical texts dating back to the period of First World War. |
DEbateNet-mig15:Tracing the 2015 Immigration Debate in Germany Over Time (2020.lrec-1)
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Gabriella Lapesa, Andre Blessing, Nico Blokker, Erenay Dayanik, Sebastian Haunss, Jonas Kuhn, Sebastian Padó
| Challenge: | a dataset for germany covering the public debate on immigration is annotated . a political science notion of a claim is used to represent the political discourse . |
| Approach: | They annotate a dataset for german public debate on immigration in 2015 using a political science notion of a claim . they identify claims in newspaper articles, assign them to actors and fine-grained categories and annotize their polarity and date. |
| Outcome: | The dataset is annotated by a political science framework and shows it captures political debate . it shows that political actors can change their positions and take a strong stand against them . |
A Corpus of Spanish Political Speeches from 1937 to 2019 (2020.lrec-1)
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| Challenge: | a corpus of political speeches in Spanish is documented from 1937 to 2019 . the corpus contains the speeches delivered by the head of state of Spain on Christmas Eve . |
| Approach: | They propose to collect political speeches from the Christmas Eve national speeches from 1937 to 2019 . they propose a Python interface that allows querying and analyzing the corpus . |
| Outcome: | The proposed corpus contains speeches delivered by the king of Spain from 1937 to 2019 . the documents reflect some of the most significant events and political changes in recent history . a set of HTML visualizations is provided to navigate the corpus and explore differences between TF-IDF frequencies. |
A New Latin Treebank for Universal Dependencies: Charters between Ancient Latin and Romance Languages (2020.lrec-1)
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| Challenge: | Currently, Latin features the most data and the most treebanks of all the ancient languages of UD . |
| Approach: | They introduce a Latin treebank that follows the Universal Dependencies (UD) annotation standard . they use a translation of the late Latin Charter Treebank 2 (LLCT2) into the UD style . |
| Outcome: | The proposed treebank is based on the Universal Dependencies (UD) annotation standard. |
Identification of Indigenous Knowledge Concepts through Semantic Networks, Spelling Tools and Word Embeddings (2020.lrec-1)
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| Challenge: | Using semantic tools and network methods, dialectal variations of words are typically used to access native, regional knowledge contained in language corpora. |
| Approach: | They propose a technique for identifying dialectal variations of words in a non-standard language legacy dataset using GermaLemma. |
| Outcome: | The proposed method may be used in other projects dealing with non-standard or endangered language collections, aiming to access, analyze and ultimately preserve native regional knowledge. |
A Multi-Orthography Parallel Corpus of Yiddish Nouns (2020.lrec-1)
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| Challenge: | Existing corpora of Yiddish text are limited to a single, potentially non-standard orthography . non-phonetically spelled Hebrew words are the largest cause of error, according to our study . |
| Approach: | They propose a multi-orthography parallel Yiddish corpus based on Wiktionary scraping . they also demonstrate how the system can be used to bootstrap a transliteration model . |
| Outcome: | The proposed system achieves error rates between 16.79% and 28.47% on the test set. |
An Annotated Corpus of Adjective-Adverb Interfaces in Romance Languages (2020.lrec-1)
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| Challenge: | Open Access Database: Adjective-Adverb Interfaces in Romance is an annotated and lemmatised corpus of various linguistic phenomena related to Romance adjectives with adverbial functions. |
| Approach: | The project is an annotated and lemmatised corpus of various linguistic phenomena related to Romance adjectives with adverbial functions. |
| Outcome: | The project aims to serve linguistic research based on transparent and accessible corpus-based data. |
Language Resources for Historical Newspapers: the Impresso Collection (2020.lrec-1)
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| Challenge: | digitization efforts are slowly but steadily contributing an increasing amount of facsimiles of cultural heritage documents. |
| Approach: | They propose to use a collection of newspaper data sets composed of text and image resources, curated and published within the context of the ‘impresso - Media Monitoring of the Past’ project. |
| Outcome: | The aim of the impresso resource collection is to contribute to historical language resources, and strengthen approaches to non-standard inputs and foster efficient processing of historical documents. |
Allgemeine Musikalische Zeitung as a Searchable Online Corpus (2020.lrec-1)
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| Challenge: | specialized newspapers are not well curated in terms of digitization quality, data formatting, completeness, redundancy (de-duplication), supply of metadata, and hence, searchability. |
| Approach: | They propose a workflow that copes with a posteriori digitization problems, inconsistent OCRing and index building for searchability for a major German-language newspaper of the Romantic Age. |
| Outcome: | The proposed workflow copes with a posteriori digitization problems, inconsistent OCRing and index building for searchability. |
Stylometry in a Bilingual Setup (2020.lrec-1)
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| Challenge: | a stylometric method of comparing texts by most frequent words does not allow direct comparison of original texts and their translations, i.e. across languages. |
| Approach: | They propose a stylometric method that removes language-specific features and parses each language counterpart with a corresponding language model in UDPipe. |
| Outcome: | The proposed method removes language-specific features and keeps linguistically independent features of individual author signal. |
Dialect Clustering with Character-Based Metrics: in Search of the Boundary of Language and Dialect (2020.lrec-1)
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| Challenge: | 'A language is a dialect with an army and navy' is attributed to sociologist Max Weinrich. |
| Approach: | They propose a universal character-based method for representing sentences so that one can calculate the distance between any two sentence pairs. |
| Outcome: | The proposed method can be used to calculate distance between two sentences by clustering a dialect/sub-language mixed corpus into sub-groups and to partially answer the question of what separates languages from dialects. |
DiscSense: Automated Semantic Analysis of Discourse Markers (2020.lrec-1)
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| Challenge: | Existing models for predicting discourse markers have been used to study link between markers and semantic relations . |
| Approach: | They use a model trained to predict discourse markers between sentence pairs to predict plausible markers between sentences with a known semantic relation. |
| Outcome: | The proposed method predicts markers between sentence pairs with a known semantic relation . the resulting dataset, named DiscSense, is publicly available . |
ThemePro: A Toolkit for the Analysis of Thematic Progression (2020.lrec-1)
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| Challenge: | Thematic progression is relevant to natural language processing applications dealing with discourse structure, argumentation structure, natural language generation, summarization and topic detection. |
| Approach: | They propose a toolkit for automatic analysis of thematic progression using a web interface. |
| Outcome: | ThemePro provides a visualization of the results including syntactic trees, hierarchical thematicity over propositions and thematic progression over whole texts. |
Machine-Aided Annotation for Fine-Grained Proposition Types in Argumentation (2020.lrec-1)
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| Challenge: | a corpus of 2016 debates and commentary contains 4,648 argumentative propositions annotated with fine-grained proposition types. |
| Approach: | They propose a machine learning-human workflow for annotating for four complex proposition types . they demonstrate with preliminary analysis of rhetorical strategies and structure in presidential debates . |
| Outcome: | The proposed method can be used by technical researchers seeking more nuanced representations of argument . it can also be used to analyze rhetorical strategies and structure in presidential debates . |
Chinese Discourse Parsing: Model and Evaluation (2020.lrec-1)
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| Challenge: | Chinese discourse parsing has not yet a consistent evaluation metric . micro vs. macro F1 scores, binary v. multiway ground truth, and left-heavy v . right-heaviness binarization are important for Chinese discourses . |
| Approach: | They propose a neural network model that unifies a pre-trained transformer and a CKY-like algorithm and compare it with previous models with different evaluation scenarios. |
| Outcome: | The proposed model outperforms the previous models with different evaluation scenarios. |
Shallow Discourse Annotation for Chinese TED Talks (2020.lrec-1)
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| Challenge: | Existing methods to annotate text with discourse properties are limited to newspaper articles and are not available in Chinese. |
| Approach: | They propose to annotate TED talks with Chinese-related properties using the Penn Discourse TreeBank annotation style . they propose to use planned monologues instead of written text to annnotate Chinese-specific properties. |
| Outcome: | The proposed method is able to achieve reliable results in Chinese spoken monologues, and is based on the Penn Discourse TreeBank annotation style. |
The Discussion Tracker Corpus of Collaborative Argumentation (2020.lrec-1)
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| Challenge: | The Discussion Tracker corpus is an annotated dataset of transcripts of spoken, multi-party argumentation transcribed from 985 minutes of audio . |
| Approach: | They analyze 29 multi-party arguments transcribed from 985 minutes of audio . they provide descriptive statistics and code for predicting each dimension separately. |
| Outcome: | The Discussion Tracker corpus was collected in high school English classes and annotated for argument moves, specificity, specificities and collaboration dimensions. |
Shallow Discourse Parsing for Under-Resourced Languages: Combining Machine Translation and Annotation Projection (2020.lrec-1)
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| Challenge: | Shallow Discourse Parsing (SDP) relies on large amounts of training data, which so far exists only for English. |
| Approach: | They propose to translate an existing English Penn Discourse TreeBank into German and use it to create a German corpus annotated for shallow discourse relations in the news domain. |
| Outcome: | The proposed corpus is annotated for shallow discourse relations in the (financial) news domain. |
A Corpus of Encyclopedia Articles with Logical Forms (2020.lrec-1)
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| Challenge: | a corpus of annotated typed lambda calculus translations is described in this paper . typed Lambda Calculus expressions are intended to serve as a theory-neutral formal representation . |
| Approach: | They describe an annotated corpus of typed lambda calculus translations for 2,000 sentences in Simple English Wikipedia. |
| Outcome: | The annotated typed lambda calculus translations are used in a corpus of 2,000 sentences in Simple English Wikipedia. |
The Potsdam Commentary Corpus 2.2: Extending Annotations for Shallow Discourse Parsing (2020.lrec-1)
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| Challenge: | Potsdam Commentary Corpus 2.2 is a german corpus of news editorials annotated on several levels. |
| Approach: | They propose to add relation senses to an already existing layer of discourse connectives and their arguments and a new layer with additional coherence relation types to the potsdam commentary corpus. |
| Outcome: | The proposed corpus is more usable for shallow discourse parsing. |
On the Creation of a Corpus for Coherence Evaluation of Discursive Units (2020.lrec-1)
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| Challenge: | Most corpora for textual coherence evaluation are composed of randomly shuffled sentences that focus on sentence ordering. |
| Approach: | They propose to use a variety of corruption strategies to build a corpus of incoherent pairs of sentences by swapping their discourse connective or a discourse argument. |
| Outcome: | The proposed corpus is constructed from discourse argument pairs from the Penn Discourse Tree Bank and is compared with existing corpus models. |
Joint Learning of Syntactic Features Helps Discourse Segmentation (2020.lrec-1)
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| Challenge: | Discourse segmentation is a task of fragmenting text into minimal disjoint chunks of text called Elementary Discourse Units (EDUs). |
| Approach: | They propose a framework for multi-lingual discourse segmentation with BERT . they cast the problem as a token classification problem and jointly learn syntactic features like part-of-speech tags and dependency relations. |
| Outcome: | Experiments in English, Dutch, German, Portuguese Brazilian and Basque show that the proposed model performs better across languages. |
Creating a Corpus of Gestures and Predicting the Audience Response based on Gestures in Speeches of Donald Trump (2020.lrec-1)
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| Challenge: | a study aims to explore the role of speech pauses and gestures alone as predictors of audience reaction without other types of speech information. |
| Approach: | They analyze two speeches by Barack Obama and use them to predict audience reaction . they find that long pauses and co-speech gestures alone predict audience response . |
| Outcome: | The proposed models can predict audience reaction without other types of speech information. |
GeCzLex: Lexicon of Czech and German Anaphoric Connectives (2020.lrec-1)
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| Challenge: | Existing lexicons of connectives are interlinked with each other to provide a bilingual inventory of connective entries. |
| Approach: | They introduce the first version of a lexicon for translation equivalents of Czech and German discourse connectives. |
| Outcome: | The lexicon is the first bilingual inventory of connectives with linkage on the level of individual entries. |
DiMLex-Bangla: A Lexicon of Bangla Discourse Connectives (2020.lrec-1)
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| Challenge: | Discourse connectives are widely believed to be the most explicit, prototypical and most reliable relational signals in discourse processing. |
| Approach: | They present a newly developed lexicon of Bangla discourse connectives . it contains 123 Bangla connective entries, which are primarily compiled from literature . |
| Outcome: | The lexicon contains 123 Bangla connective entries, which are compiled from the linguistic literature and translation of English discourse connectives. |
Semi-Supervised Tri-Training for Explicit Discourse Argument Expansion (2020.lrec-1)
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| Challenge: | a novel application of semi-supervision for shallow discourse parsing is described . we focus on explicit discourse arguments, but we leave the sense selection aside . |
| Approach: | They propose a semi-supervised approach for shallow discourse parsing using sequence tagging. |
| Outcome: | The proposed approach improves performance by 2-10% in the first setting and by comparing the results with training relations. |
WikiPossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts (2020.lrec-1)
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| Challenge: | a new corpus of articles is created for the task of temporally-oriented possession . the task is open-domain and can be used to track possession in other texts . |
| Approach: | They propose a new corpus for the task of temporally-oriented possession . they annotate Wikipedia articles for 90 different well-known artifacts . |
| Outcome: | The proposed task is based on annotated Wikipedia articles for 90 artifacts, including paintings, diamonds, and archaeological artifos. |
TED-Q: TED Talks and the Questions they Evoke (2020.lrec-1)
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| Challenge: | Evoked questions represent a hitherto unexplored type of linguistic data, promising to open up important new lines of research. |
| Approach: | They propose a method to annotate TED-talks with the questions they evoke and, where available, the answers to these questions. |
| Outcome: | The proposed method is designed to scale up, relying on crowdsourcing by non-expert annotators, with its utility for Natural Language Processing in mind. |
CzeDLex 0.6 and its Representation in the PML-TQ (2020.lrec-1)
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| Challenge: | CzeDLex is an electronic lexicon of Czech discourse connectives . its data comes from a large treebank annotated with discourse relations . |
| Approach: | They present a new electronic lexicon of Czech discourse connectives with data coming from a large treebank annotated with discourse relations. |
| Outcome: | The lexicon is available on-line and released under the Creative Commons License. |
Corpus for Modeling User Interactions in Online Persuasive Discussions (2020.lrec-1)
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| Challenge: | Several studies have focused on the identification and classification of argumentative components and the argumentative relations between the components. |
| Approach: | They propose an annotation scheme and corpus that captures user-generated inner-post arguments and inter-post relations between users in ChangeMyView. |
| Outcome: | The proposed annotation scheme captures user-generated inner-post arguments and inter-post relations in ChangeMyView, a persuasive forum. |
Simplifying Coreference Chains for Dyslexic Children (2020.lrec-1)
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| Challenge: | Existing systems to generate adapted content for dyslexic children for French address specific audiences. |
| Approach: | They propose a system to transform texts at the discourse level by using rules to modify coreference chains, which are markers of text cohesion, in the context of the ALECTOR project. |
| Outcome: | The proposed system can generate adapted content for dyslexic children for French, in the context of the ALECTOR project. |
Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives (2020.lrec-1)
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| Challenge: | Existing studies on the performance of BERT for implicit discourse relation classification have not been conducted. |
| Approach: | They propose to apply BERT to implicit discourse relation classification by performing additional pre-training on text tailored to discourse relations. |
| Outcome: | The proposed methods outperform previous state-of-the-art models in many tasks. |
What Speakers really Mean when they Ask Questions: Classification of Intentions with a Supervised Approach (2020.lrec-1)
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| Challenge: | Existing work on hidden intentions of speakers in questions during meals is based on written or oral data, which are less easy to interpret. |
| Approach: | They propose a typology of hidden intentions in questions asked during meals . they implement an automatic classification model based on annotated data and selected linguistic features. |
| Outcome: | The proposed model is based on annotated data and features and evaluates its performance. |
Modeling Dialogue in Conversational Cognitive Health Screening Interviews (2020.lrec-1)
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| Challenge: | Dementia is one of the most pressing healthcare concerns as median age rises . a conversational agent capable of conducting cognitive health screening interviews could be an inexpensive, flexible, low-stress alternative . |
| Approach: | They propose an annotation schema for assigning dialogue act labels to utterances in patient-interviewer conversations collected as part of a clinically-validated cognitive health screening task. |
| Outcome: | The proposed system is characterized by high inter-annotator agreement and is able to perform clinically-validated cognitive health screening tasks. |
Stigma Annotation Scheme and Stigmatized Language Detection in Health-Care Discussions on Social Media (2020.lrec-1)
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| Challenge: | a large amount of research has been done on the interpretation and influence of stigma on human behaviour and health. |
| Approach: | They develop an annotation scheme and improve the annotation process for stigma identification . they aim to distinguish stigmatised language from non-stigmatised using machine learning and NLP . |
| Outcome: | The proposed method improves the annotation process for stigma identification . the results show that the method performs better than other models . |
An Annotated Dataset of Discourse Modes in Hindi Stories (2020.lrec-1)
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Swapnil Dhanwal, Hritwik Dutta, Hitesh Nankani, Nilay Shrivastava, Yaman Kumar, Junyi Jessy Li, Debanjan Mahata, Rakesh Gosangi, Haimin Zhang, Rajiv Ratn Shah, Amanda Stent
| Challenge: | Using a new corpus of sentences from Hindi short stories, we analyze the annotations for five different discourse modes argumentative, narrative, descriptive, dialogic and informative. |
| Approach: | They propose to annotate sentences from Hindi short stories for five different discourse modes argumentative, narrative, descriptive, dialogic and informative. |
| Outcome: | The proposed corpus has a high inter-annotator agreement (0.87 k-alpha) and is able to capture the nuances of the embedded discourse structures. |
Multi-class Multilingual Classification of Wikipedia Articles Using Extended Named Entity Tag Set (2020.lrec-1)
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| Challenge: | Existing classification models struggle with large datasets using fine-grained tag sets. |
| Approach: | They propose to structure Wikipedia into a large multi-lingual dataset using an Extended Named Entity tag set. |
| Outcome: | The proposed model fails to describe why Wikipedia articles are used to summarize, translate or answer questions. |
An Algerian Corpus and an Annotation Platform for Opinion and Emotion Analysis (2020.lrec-1)
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| Challenge: | Currently, there are more than 4 billion Internet users worldwide . the number of social media users in Algeria has tripled over a year . |
| Approach: | They propose a platform for crowdsourcing annotation of tweets at different levels of granularity. |
| Outcome: | The proposed platform can be used to create the largest Algerian dialect subjectivity lexicon of about 9,000 entries. |
Transfer Learning from Transformers to Fake News Challenge Stance Detection (FNC-1) Task (2020.lrec-1)
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| Challenge: | In the last two years, significant improvements have occurred in NLP with the development of large language models using contextualized word embeddings based on the Google Transformer architecture. |
| Approach: | They performed experiments on data from the Fake News Challenge stage 1 (FNC-1) they used BERT sentence embeddings as a model feature and BERT, XLNet, and RoBERTa transformers to fine-tune them. |
| Outcome: | The proposed model outperforms the winner's system on class-wise F1 scores and achieves state-of-the-art on the stance detection task. |
Scientific Statement Classification over arXiv.org (2020.lrec-1)
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| Challenge: | a dataset of 1.2 million documents converted from the original submissions is available for supervised learning. |
| Approach: | They propose a new classification task for scientific statements and a large-scale dataset for supervised learning. |
| Outcome: | The proposed task achieves a 0.91 F1 score and a lexeme serialization for mathematical formulas. |
Cross-domain Author Gender Classification in Brazilian Portuguese (2020.lrec-1)
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| Challenge: | Author profiling models predict demographic characteristics of a target author based on the text that they have written. |
| Approach: | They propose to use a cross-domain gender classification task in the Brazilian Portuguese language to test author profiling models. |
| Outcome: | The proposed model uses word- and psycholinguistics-based features alike to predict gender, age, personality traits and other characteristics of a target author. |
LEDGAR: A Large-Scale Multi-label Corpus for Text Classification of Legal Provisions in Contracts (2020.lrec-1)
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| Challenge: | Contractual provisions are a primary research target in law studies as they constitute the legal essence of a contract. |
| Approach: | They propose to use LEDGAR to construct a multilabel corpus of legal provisions in contracts that is crawled and scraped from the public domain. |
| Outcome: | The proposed corpus is the first freely available corpus of its kind. |
Online Near-Duplicate Detection of News Articles (2020.lrec-1)
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| Challenge: | Near-duplicate documents are prevalent in news corpora and cost significant . bloating corporata with redundant information and computational costs are among the costs . |
| Approach: | They propose an online system which flags a near-duplicate document by finding its most likely original. |
| Outcome: | The proposed system can be used in many real-world applications. |
Automated Essay Scoring System for Nonnative Japanese Learners (2020.lrec-1)
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| Challenge: | Existing systems only provide a holistic score that summarizes the quality of an essay, which provides little feedback for a language learner. |
| Approach: | They developed an automated essay scoring system for Japanese as a second language learners using an essay dataset with annotations for a holistic score and multiple trait scores. |
| Outcome: | The proposed system achieves the highest accuracy in various natural language processing tasks. |
A Real-World Data Resource of Complex Sensitive Sentences Based on Documents from the Monsanto Trial (2020.lrec-1)
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| Challenge: | Sensitive information detection is of great importance in a number of applications where unintended leaks of sensitive information may incur severe negative consequences. |
| Approach: | They propose to use a corpus of sentences to evaluate sensitive information detection approaches . they employ human annotations and automatically infer labels from domain experts . |
| Outcome: | The proposed models are based on a monsanto trial and are evaluated on sentence level. |
Discovering Biased News Articles Leveraging Multiple Human Annotations (2020.lrec-1)
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| Challenge: | Political propaganda and one-sided views can be found in the news and can cause distrust in media. |
| Approach: | They propose to annotate politically biased news articles by an algorithm annotated by domain experts and crowd workers and to compare them to crowd workers. |
| Outcome: | The proposed method compares domain experts to crowd workers and shows that bias can be detected automatically. |
Corpora and Baselines for Humour Recognition in Portuguese (2020.lrec-1)
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| Challenge: | Existing work on the recognition of verbal humour in Portuguese has not been done . humor recognition is a sign of fluency in a language, and is not yet widely used in other languages. |
| Approach: | They propose to create three corpora covering two styles of humour and four sources of non-humorous text that are used for testing computational models. |
| Outcome: | The proposed models can be used to train and test models in Portuguese, and may be used as baselines for future projects. |
FactCorp: A Corpus of Dutch Fact-checks and its Multiple Usages (2020.lrec-1)
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| Challenge: | Fact-checking information before publication has long been a core task for journalists, but recent times have seen the emergence of dedicated news items specifically aimed at fact-checks after publication. |
| Approach: | They propose to study fact-checks from a corpus linguistic perspective and to create a textual corpus that contains 1,974 fact- checks from three major Dutch newspapers. |
| Outcome: | The proposed method can be applied to scientific communication landscapes and to the media. |
Automatic Orality Identification in Historical Texts (2020.lrec-1)
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| Challenge: | a set of general linguistic features are used to identify conceptually-oral historical texts . linguists recognize that there is also a lot of variation within discourse modes . |
| Approach: | They propose to use general linguistic features to identify conceptually-oral historical texts . they find they are useful for determining conceptuality of historical data as for modern data . |
| Outcome: | The proposed features are used to identify conceptually-oral historical German texts . the features are useful in determining conceptuality of historical data as they are for modern data . |
Using Deep Neural Networks with Intra- and Inter-Sentence Context to Classify Suicidal Behaviour (2020.lrec-1)
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Xingyi Song, Johnny Downs, Sumithra Velupillai, Rachel Holden, Maxim Kikoler, Kalina Bontcheva, Rina Dutta, Angus Roberts
| Challenge: | Mental health problems are a major risk factor for suicide attempts. |
| Approach: | They propose to integrate information from sentences to left and right of the target sentence into the model to improve classification accuracy. |
| Outcome: | The proposed model was able to classify suicidal behaviour in autism spectrum disorder patient records significantly better than previous approaches. |
A First Dataset for Film Age Appropriateness Investigation (2020.lrec-1)
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| Challenge: | Several studies in CL have examined the genre in terms of Sentiment Analysis (Phan and Matsumoto, 2018) . but the actual film content (the script, audio, and video for example) has not received as much interest in spite of the potential availability of huge datasets. |
| Approach: | They use a corpus of 17000 films along with their age ratings to predict the correct age classification for the United States and the UK. |
| Outcome: | The proposed model outperforms FastText and Deep Learning architectures in the US and UK and is 79.3% accurate for the US compared to 80% for the UK. |
Habibi - a multi Dialect multi National Arabic Song Lyrics Corpus (2020.lrec-1)
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| Challenge: | Unlike western music, Arabic songs are poorly classified and the majority of the songs available online are classified under Modern Arabic Pop genre or what is now known as Franco-Arabic . |
| Approach: | They introduce Habibi the first Arabic Song Lyrics corpus for singers from 18 different Arabic countries. |
| Outcome: | The proposed corpus contains more than 30,000 Arabic song lyrics in 6 Arabic dialects for singers from 18 different arab countries. |
Age Suitability Rating: Predicting the MPAA Rating Based on Movie Dialogues (2020.lrec-1)
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| Challenge: | Using the MPAA rating, movie content can negatively affect children’s behaviour, for example, watching specific programs may encourage irresponsible sexual behavior and alcohol usage in teenagers. |
| Approach: | They propose an RNN-based architecture that jointly models the genre and the emotions in the script to predict the MPAA rating. |
| Outcome: | The proposed model outperforms the traditional machine learning method by 7% and achieves an 81% weighted F1 score. |
Email Classification Incorporating Social Networks and Thread Structure (2020.lrec-1)
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| Challenge: | Existing methods for document classification in social networks capture only semantics of texts . incorporating social network information in addition to textual information is effective . |
| Approach: | They propose to incorporate social network information into document classification tasks . they use email as a feature and model email thread structure . |
| Outcome: | The proposed method improves over a state-of-the-art baseline based on textual information . the proposed method is based in two corpora, one of which we train on . |
Development and Validation of a Corpus for Machine Humor Comprehension (2020.lrec-1)
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| Challenge: | a Chinese humor corpus was labeled with five levels of funniness, eight skill sets of humor, and six dimensions of intent by only one annotator. |
| Approach: | They develop a Chinese humor corpus with 3,365 jokes labeled with five levels of funniness, eight skill sets of humor, and six dimensions of intent by only one annotator. |
| Outcome: | The proposed corpus contains 3,365 jokes from over 40 sources. |
Alector: A Parallel Corpus of Simplified French Texts with Alignments of Misreadings by Poor and Dyslexic Readers (2020.lrec-1)
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| Challenge: | Typical readers tend to progress quickly in reading because of the automatic process, which increases word identification and vice-versa. |
| Approach: | They propose a parallel corpus for reading tests and for the development of automatic text simplification tools for children with reading difficulties. |
| Outcome: | The proposed corpus is available for consultation through a web interface and available on demand for research purposes. |
A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients (2020.lrec-1)
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Edward T. Moseley, Joy T. Wu, Jonathan Welt, John Foote, Patrick D. Tyler, David W. Grant, Eric T. Carlson, Sebastian Gehrmann, Franck Dernoncourt, Leo Anthony Celi
| Challenge: | Currently, most medical data is generated and stored in unstructured, text-based format. |
| Approach: | They propose to use a patient phenotyping dataset to identify whether a given medical condition is present in their notes. |
| Outcome: | The proposed dataset contains 1102 Discharge Summaries and 1000 Nursing Progress Notes. |
Multilingual Stance Detection in Tweets: The Catalonia Independence Corpus (2020.lrec-1)
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| Challenge: | stance detection is a method to determine the attitude of a text with respect to a specific topic or claim. |
| Approach: | They propose a multilingual dataset for stance detection in Twitter for the Catalan and Spanish languages. |
| Outcome: | The proposed dataset shows that it is well balanced for multilingual and cross-lingual research. |
An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing (2020.lrec-1)
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Abdul Moeed, Gerhard Hagerer, Sumit Dugar, Sarthak Gupta, Mainak Ghosh, Hannah Danner, Oliver Mitevski, Andreas Nawroth, Georg Groh
| Challenge: | Fine-tuning suffers from catastrophic forgetting, a problem exacerbated in natural language processing (NLP). |
| Approach: | They propose to use progressive neural networks to re-use previously learned knowledge when learning new tasks. |
| Outcome: | The proposed approach improves on common NLP tasks across a range of architectures, datasets, and tasks. |
WAC: A Corpus of Wikipedia Conversations for Online Abuse Detection (2020.lrec-1)
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| Challenge: | Existing methods for moderation of abusive content are limited by the lack of large corpora of conversations. |
| Approach: | They propose a framework with comment-level abuse annotations based on the Wikipedia Comment corpus . they propose 'context-based' approaches to detect abusive content based upon conversational context . |
| Outcome: | The proposed framework can be used to improve the moderation process of abusive content on the Internet. |
FloDusTA: Saudi Tweets Dataset for Flood, Dust Storm, and Traffic Accident Events (2020.lrec-1)
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| Challenge: | Detecting events from tweets can help to predict real-world events precisely. |
| Approach: | They propose to use tweets written in Arabic and Saudi dialects to identify events from tweets. |
| Outcome: | The proposed system can detect flood, dust storm, traffic accident, and non-event. |
An Annotated Corpus for Sexism Detection in French Tweets (2020.lrec-1)
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Patricia Chiril, Véronique Moriceau, Farah Benamara, Alda Mari, Gloria Origgi, Marlène Coulomb-Gully
| Challenge: | Social media networks allow users to share opinions and sentiments, which can cause a large spreading of hatred or abusive messages. |
| Approach: | They propose to annotate 12,000 tweets with a sexism detection scheme in France . they propose to use deep learning to detect if a message with sexist content is really s. |
| Outcome: | The proposed scheme detects sexist content and identifies if it is really sexism . the proposed scheme is the first of its kind in the u.s. |
Measuring the Impact of Readability Features in Fake News Detection (2020.lrec-1)
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Roney Santos, Gabriela Pedro, Sidney Leal, Oto Vale, Thiago Pardo, Kalina Bontcheva, Carolina Scarton
| Challenge: | Recent efforts to detect fake news use language-based approaches to detect news articles . authors show that readability features can improve classification accuracy . |
| Approach: | They propose to use readability features to detect fake news in the Brazilian Portuguese language . they show that such features can achieve up to 92% classification accuracy . |
| Outcome: | The proposed features achieve up to 92% accuracy and may improve previous classification results. |
When Shallow is Good Enough: Automatic Assessment of Conceptual Text Complexity using Shallow Semantic Features (2020.lrec-1)
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| Challenge: | Existing approaches to automatic assessment of text complexity focus on syntactic and lexical complexity. |
| Approach: | They propose to use graph-based deep semantic features to automatically assess conceptual text complexity by using DBpedia as a proxy to human knowledge. |
| Outcome: | The proposed features outperform the state-of-the-art features on pairwise comparison of two versions of the same text and five-level classification task. |
DecOp: A Multilingual and Multi-domain Corpus For Detecting Deception In Typed Text (2020.lrec-1)
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| Challenge: | Recent studies show that humans are ineffective in spotting deceit, with accuracy rates only slightly above the chance level. |
| Approach: | They propose a new language resource for automatic deception detection in cross-domain and cross-language scenarios. |
| Outcome: | The proposed language resource is composed of 5000 examples of truthful and deceitful first-person opinions across five different domains and two languages. |
Age Recommendation for Texts (2020.lrec-1)
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| Challenge: | adequacy of a text’s characteristics with the person’s capacities and knowledge is critical in the case of . a child since her/his cognitive and linguistic skills are still under development. |
| Approach: | They propose a natural language processing task which consists in predicting the age from which a text can be understood by someone. |
| Outcome: | The proposed model outperforms psycholinguist models on a French text dataset and shows that the results are more accurate than psycholingual models. |
Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition (2020.lrec-1)
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| Challenge: | Existing work on document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes. |
| Approach: | They assemble and publish a multilingual Twitter corpus for the task of hate speech detection using inferred author demographic factors. |
| Outcome: | The results show that the classifiers learn human biases and can be discriminatory towards certain demographic groups. |
VICTOR: a Dataset for Brazilian Legal Documents Classification (2020.lrec-1)
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Pedro Henrique Luz de Araujo, Teófilo Emídio de Campos, Fabricio Ataides Braz, Nilton Correia da Silva
| Challenge: | Approximately 10% of these are unstructured and requiring a lot of time to sort through. |
| Approach: | They propose to use a dataset built from Brazil's Supreme Court digitalized legal documents to improve document type classification and theme assignment tasks. |
| Outcome: | The proposed dataset is based on 45 thousand appeals and contains roughly 692 thousand documents—about 4.6 million pages. |
Dynamic Classification in Web Archiving Collections (2020.lrec-1)
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| Challenge: | a growing number of research libraries, museums, and archives are embracing Web Archiving as a mechanism to collect born-digital material made available via the Web. |
| Approach: | They propose to use dynamic fusion models to find the model that performs best on a variety of document types. |
| Outcome: | The proposed model outperforms individual models and other ensemble methods on three datasets. |
Aspect Flow Representation and Audio Inspired Analysis for Texts (2020.lrec-1)
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| Challenge: | Observing how a text exploits a particular aspect can lead to significant information loss, especially for large texts. |
| Approach: | They propose a method of representing and analysing texts that consider how an aspect behaves throughout the text by aspect flows. |
| Outcome: | The proposed method surpasses summarised features in classification tasks and reveals deeper knowledge about the represented texts. |
Annotating and Analyzing Biased Sentences in News Articles using Crowdsourcing (2020.lrec-1)
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| Challenge: | a lack of publicly available news bias datasets has hindered efforts to detect subtle biases in news articles. |
| Approach: | They propose a news bias dataset which contains sentences with bias labels . they propose to use the dataset to develop and evaluate methods for detecting news bias . |
| Outcome: | The proposed dataset can be used for analyzing news bias and for developing and evaluating methods for news bias detection. |
Evaluation of Deep Gaussian Processes for Text Classification (2020.lrec-1)
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| Challenge: | Existing models for text classification are limited by the expressability limit and require enormous empirical efforts to come up with a robust model architecture. |
| Approach: | They propose a Bayesian non-parametric Bayessian nonparametric model with strong function compositionality for the task of Text Classification. |
| Outcome: | The proposed models outperform shallow and deep Gaussian processes on the TREC (Text REtrieval Conference) datasets. |
EmoEvent: A Multilingual Emotion Corpus based on different Events (2020.lrec-1)
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| Challenge: | In recent years, emotion detection in text has become more popular due to its potential applications in fields such as psychology, marketing, political science, among others. |
| Approach: | They propose to use an annotated dataset to identify emotions in tweets from different events that took place in April 2019 to validate the effectiveness of the data set. |
| Outcome: | The proposed method is based on a multilingual emotion data set based in different events that took place in April 2019 in English and Spanish. |
MuSE: a Multimodal Dataset of Stressed Emotion (2020.lrec-1)
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| Challenge: | Existing studies on the effects of stress and emotion on the production and perception of emotion are understudied. |
| Approach: | They propose to use a multimodal stressed emotion dataset to study the interplay between the presence of stress and expressions of affect. |
| Outcome: | The proposed dataset combines emotion and stress classification with annotations for the emotional content of the recordings. |
Affect inTweets: A Transfer Learning Approach (2020.lrec-1)
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| Challenge: | Existing machine learning models require considerable effort to design task specific features to understand affectual states of people. |
| Approach: | They propose a transfer-learning based approach to infer the affectual state of a person from tweets. |
| Outcome: | The proposed model ranks 2nd, 4th and 6th in four of the four subtasks on SemEval-2018 task 1: Affect in Tweets. |
Annotation of Emotion Carriers in Personal Narratives (2020.lrec-1)
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| Challenge: | PNs are recollections of facts, events, and thoughts that are best explained by speech or text segments . spoken PN is difficult because it is unstructured and involving multiple sub-events and characters as well as thoughts and associated emotions perceived by the narrator. |
| Approach: | They propose and evaluate an annotation model for identifying emotion carriers in spoken personal narratives from the Ulm State-of-Mind in Speech corpus. |
| Outcome: | The proposed model could be used to extract emotion carriers from spoken personal narratives, which are often unstructured and often unorganized . |
Towards Interactive Annotation for Hesitation in Conversational Speech (2020.lrec-1)
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| Challenge: | recognizing affects in spontaneous, non acted speech is expensive in both human resources and time. |
| Approach: | They aim to automatize the labeling of hesitant speech as a marker of expressed uncertainty. |
| Outcome: | The proposed method shows that the number of filled pauses and vowel duration increases with the degree of hesitation, and that automatic prediction of the hesitation degree reaches encouraging RMSE results of 1.6. |
Abusive language in Spanish children and young teenager’s conversations: data preparation and short text classification with contextual word embeddings (2020.lrec-1)
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| Challenge: | Existing studies on how to automatically detect abusive short texts are gaining interest in the natural language processing community. |
| Approach: | They propose to use a contextual word embedding model to automatically detect abusive short texts for Spanish language. |
| Outcome: | The proposed model outperforms classical methods in the detection of abusive short texts for the spanish language. |
IIIT-H TEMD Semi-Natural Emotional Speech Database from Professional Actors and Non-Actors (2020.lrec-1)
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| Challenge: | Existing databases for emotion recognition are limited due to privacy and legal issues. |
| Approach: | They propose to collect emotional speech data from actors and non-actors using designed drama situations and annotate them manually using a hybrid strategy. |
| Outcome: | The proposed database is based on simulated parallel, semi-natural, and (near to) natural databases. |
The POTUS Corpus, a Database of Weekly Addresses for the Study of Stance in Politics and Virtual Agents (2020.lrec-1)
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| Challenge: | Embodied Conversational Agents (ECAs) are used to generate socially believable agents. |
| Approach: | They propose to use audio-video files of political addresses to generate a corpus of socially believable agents which can be annotated by external observers. |
| Outcome: | The proposed corpus analyzes audio-video files of political addresses to the american people and provides the same speeches given by a virtual agent named Rodrigue. |
GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception (2020.lrec-1)
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| Challenge: | Fewer studies address emotions as a phenomenon to be tackled with structured learning, which can be explained by the lack of relevant datasets. |
| Approach: | They propose to annotate 5000 English news headlines with their associated emotions, the corresponding emotion experiencers and textual cues, related emotion causes and targets, and the reader’s perception of the emotion of the headline. |
| Outcome: | The proposed method enables further research on emotion classification, emotion intensity prediction, emotion cause detection and supports qualitative studies. |
SOLO: A Corpus of Tweets for Examining the State of Being Alone (2020.lrec-1)
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| Challenge: | Psychologists distinguish between the concept of solitude, a positive state of voluntary aloneness, and the concept 'loneliness', characterized as dissatisfaction with the quality of one’s social interactions. |
| Approach: | They present a corpus of over 4 million tweets with query terms solitude, lonely, and loneliness. |
| Outcome: | The proposed analysis analyzes over 4 million tweets with the terms solitude, lonely, and loneliness. |
PoKi: A Large Dataset of Poems by Children (2020.lrec-1)
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| Challenge: | a new corpus of child-written texts is available for study of child language . authors use non-parametric regressions to model developmental differences from early childhood to late-adolescence . |
| Approach: | They propose to analyze 62 thousand child-written poems written by children from grades 1 to 12 . they use non-parametric regressions to model developmental differences from early childhood to late-adolescence . |
| Outcome: | The proposed corpus includes about 62 thousand poems written by children from grades 1 to 12 . results show decreases in valence that are especially pronounced during mid-adolescence . |
AlloSat: A New Call Center French Corpus for Satisfaction and Frustration Analysis (2020.lrec-1)
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| Challenge: | Existing systems retrieve emotional information from textual transcriptions or from audio signal. |
| Approach: | They propose to use a call center corpus that is continuously annotated in frustration and satisfaction to model the continuous aspect of semantic and paralinguistic information at the conversation level. |
| Outcome: | The proposed system can model the paralinguistic aspect of semantic and paralinguistic information at the conversation level. |
Learning the Human Judgment for the Automatic Evaluation of Chatbot (2020.lrec-1)
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| Challenge: | Existing evaluation methods for dialogue systems rely on human judges to label quality of generated text. |
| Approach: | They propose a machine learning approach to reduce the effort of human evaluation by learning the human judgment on comparing two generative dialogue systems. |
| Outcome: | The proposed method reduces the effort of human evaluation by learning which generative models is better in each dialog context. |
Korean-Specific Emotion Annotation Procedure Using N-Gram-Based Distant Supervision and Korean-Specific-Feature-Based Distant Supervision (2020.lrec-1)
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| Challenge: | Existing methods to annotate unlabeled data with emotions are expensive and time-consuming. |
| Approach: | They propose an annotation procedure that leverages Korean emotion lexicons and Korean-specific emotion features to annotate unlabeled data. |
| Outcome: | The proposed procedure compares with the KTEA dataset and a large-scale emotion-labeled dataset. |
Semi-Automatic Construction and Refinement of an Annotated Corpus for a Deep Learning Framework for Emotion Classification (2020.lrec-1)
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| Challenge: | Existing methods for emotion classification are expensive and require a large corpus of data. |
| Approach: | They propose a method for creating a semi-automatically constructed emotion corpus by correcting errors in the corpus. |
| Outcome: | The proposed method improves the quality of the emotion labels by correcting errors. |
CEASE, a Corpus of Emotion Annotated Suicide notes in English (2020.lrec-1)
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| Challenge: | Existing studies on suicide notes have not explored the topic of emotion detection. |
| Approach: | They develop a fine-grained emotion annotated corpus of suicide notes in English and use it to perform emotion detection on a curated dataset. |
| Outcome: | The proposed model performs emotion detection on a curated dataset of 205 suicide notes in English. |
Training a Broad-Coverage German Sentiment Classification Model for Dialog Systems (2020.lrec-1)
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| Challenge: | Existing sentiment data sets are not available for sentiment analysis. |
| Approach: | They propose to combine a German sentiment corpus with existing resources to train a general-purpose German sentiment classification model. |
| Outcome: | The proposed model trains a general-purpose German sentiment classification model . the data set contains 5.4 million labelled samples . |
An Event-comment Social Media Corpus for Implicit Emotion Analysis (2020.lrec-1)
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| Challenge: | Existing methods for identifying implicit emotions have been poor in analyzing explicit emotions. |
| Approach: | They propose to construct a Chinese eventcomment social media emotion corpus which deals with both explicit and implicit emotions with more emphasis being placed on the implicit ones. |
| Outcome: | The proposed corpus will be useful for both explicit and implicit emotion classification and detection as well as event classification. |
An Emotional Mess! Deciding on a Framework for Building a Dutch Emotion-Annotated Corpus (2020.lrec-1)
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| Challenge: | Existing frameworks for emotion recognition are limited and do not allow for categorical versus dimensional oppositions. |
| Approach: | They propose to use the emotions joy, love, anger, sadness and fear as well as dimensional models to annotate texts from different domains and topics. |
| Outcome: | The proposed frameworks are well-suited to annotate texts from different domains and topics, but the connotation of the labels strongly depends on the origin of the texts. |
PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry (2020.lrec-1)
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| Challenge: | a new study shows that literature enables engagement in a broader range of complex and subtle emotions. |
| Approach: | They propose to use multiple emotion labels to capture mixed emotions in poetry . they evaluate an annotation experiment with experts and crowdsourcing . |
| Outcome: | The proposed method shows that identifying aesthetic emotions is challenging in the German subset. |
Learning Word Ratings for Empathy and Distress from Document-Level User Responses (2020.lrec-1)
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| Challenge: | Emotion analysis of text is increasing in popularity in NLP, however, manually creating lexica for psychological constructs such as empathy has proven difficult. |
| Approach: | They compare different approaches to learning word ratings from higher-level supervision and use a Mixed-Level Feed Forward Network to create the first-ever empathy lexicon. |
| Outcome: | The proposed model automatically creates empathy word ratings from document-level ratings. |
Evaluation of Sentence Representations in Polish (2020.lrec-1)
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| Challenge: | Existing methods for learning sentence representations have been limited in low-resource languages such as Polish . |
| Approach: | They propose two new Polish datasets for evaluating sentence embeddings and evaluate eight different methods including Polish and multilingual models. |
| Outcome: | The proposed methods show strengths and weaknesses in Polish and multilingual models. |
Identification of Primary and Collateral Tracks in Stuttered Speech (2020.lrec-1)
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| Challenge: | Disfluency detection is a challenging task because of its different metrics depending on whether the input features are text or speech. |
| Approach: | They propose a framework for disfluency detection inspired by the clinical and the natural language processing perspective together with the theory of performance from (Clark, 1998) . they present a forced-aligned disfluence dataset and propose new audio features inspired by word-based span features. |
| Outcome: | The proposed framework outperforms baselines for speech-based predictions on a forced-aligned disfluency dataset from semi-directed interviews. |
How to Compare Automatically Two Phonological Strings: Application to Intelligibility Measurement in the Case of Atypical Speech (2020.lrec-1)
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| Challenge: | Atypical speech productions must be evaluated with regard to "typical" or "expected" productions . a first test of this method among healthy speakers and patients treated for cancer has proved its validity . |
| Approach: | They propose a method to evaluate "atypical" speech productions based on phonological transcriptions . authors propose to use phonology to compute distances between phonologic forms produced and expected . |
| Outcome: | The proposed method has been validated in a large population of healthy speakers and patients with cancer . it computes distances between phonological forms produced and expected from cost matrices based on features of phonemes . |
Evaluating Text Coherence at Sentence and Paragraph Levels (2020.lrec-1)
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| Challenge: | Existing text ordering models have been used to test coherence in NLP for a long time. |
| Approach: | They propose to perform paragraph ordering task and sentence ordering by using four corpora from different domains. |
| Outcome: | The proposed model performs better under certain extreme conditions than the most prevalent metric used before. |
HardEval: Focusing on Challenging Tokens to Assess Robustness of NER (2020.lrec-1)
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| Challenge: | Named entity recognition (NER) systems are often evaluated on human annotations . a new evaluation method focuses on subsets of tokens that represent specific sources of errors . |
| Approach: | They propose a method that focuses on subsets of tokens that represent specific sources of errors. |
| Outcome: | The proposed evaluation method focuses on subsets of tokens that represent specific sources of errors. |
An Evaluation Dataset for Identifying Communicative Functions of Sentences in English Scholarly Papers (2020.lrec-1)
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| Challenge: | Formulaic expressions are used by authors of scientific papers because they convey specific communicative functions in the rhetorical structure of papers. |
| Approach: | They created a manually annotated dataset to detect formulaic expressions in sentences using a seed list of labelled formulaic words. |
| Outcome: | The proposed dataset can detect communicative functions in sentences using a seed list of labelled expressions from scholarly papers in the ACL Anthology. |
An Automatic Tool For Language Evaluation (2020.lrec-1)
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| Challenge: | standardized tests are used to assess and screen developmental language impairments but require manual laborious transcription, annotation and calculation. |
| Approach: | They propose to use the correct sentence and the sentence produced by patients to evaluate the level of verbal production and return a score. |
| Outcome: | The proposed system evaluates the level of the verbal production and returns a score. |
Which Evaluations Uncover Sense Representations that Actually Make Sense? (2020.lrec-1)
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| Challenge: | Existing sense representations fail for human-centric tasks like inspecting a language’s sense inventory. |
| Approach: | They propose a coherence evaluation for sense embeddings and a model optimized for finding interpretable sense representations that are more coherent than existing sense embeds. |
| Outcome: | The proposed model is more coherent than existing sense embeddings and offers comparable word similarities with multisense representations while learning more distinguishable, interpretable senses. |
Diversity, Density, and Homogeneity: Quantitative Characteristic Metrics for Text Collections (2020.lrec-1)
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| Challenge: | Existing descriptive statistics are inadequate to summarize text collections by quantitative measures. |
| Approach: | They propose a set of characteristic metrics that quantitatively measure the dispersion, sparsity, and uniformity of a text collection. |
| Outcome: | The proposed metrics are highly correlated with text classification performance of a renowned model, which could inspire future applications. |
Towards Few-Shot Event Mention Retrieval: An Evaluation Framework and A Siamese Network Approach (2020.lrec-1)
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| Challenge: | Existing methods for event extraction are "one size fits all" and are not adaptable to new event types or domains of interest. |
| Approach: | They propose a few-shot Event Mention Retrieval task to retrieve event mentions from text . they use existing event datasets such as ACE and a Siamese Network approach . |
| Outcome: | The proposed approach lowers the bar of specifying event-centric information needs. |
Linguistic Appropriateness and Pedagogic Usefulness of Reading Comprehension Questions (2020.lrec-1)
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| Challenge: | Existing evaluation measures for automatic generation of reading comprehension questions focus on linguistic quality only, ignoring educational value and appropriateness of questions. |
| Approach: | They propose a new evaluation scheme where questions are structured in a hierarchical way . they also create and evaluate two new evaluation data sets for Basque and German . |
| Outcome: | The proposed evaluation scheme can be applied, but expert annotators are needed. |
Dataset Reproducibility and IR Methods in Timeline Summarization (2020.lrec-1)
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| Challenge: | Timeline summarization (TLS) generates a dated overview of real-world events based on event-specific corpora. |
| Approach: | They propose to use IR methods to construct event-specific corpora from a newsroom dataset . they advocate for integrating IR into the development of TLS systems . |
| Outcome: | The proposed method is not reproducible at different search times and uses components that are not always available for large news corpus. |
Database Search vs. Information Retrieval: A Novel Method for Studying Natural Language Querying of Semi-Structured Data (2020.lrec-1)
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| Challenge: | Traditionally, technicians had crafted complicated search commands in languages such as SQL to retrieve results from relational databases. |
| Approach: | They propose to map a database schema to a keyword-enabled relational database system that can be directly compared with information retrieval. |
| Outcome: | The proposed approach compares SODA, a keyword-enabled relational database system, against the Terrier information retrieval system. |
Why Attention is Not Explanation: Surgical Intervention and Causal Reasoning about Neural Models (2020.lrec-1)
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| Challenge: | a recent study finds brittleness in explanations obtained through attention mechanisms . a philosophy of science theory allows robust yet non-causal reasoning in explanation . |
| Approach: | They propose to use philosophy of science to examine the state-of-the-art in explanation for NLP models . they argue that it is impossible to explain attention-based learning by attention mechanisms . |
| Outcome: | The proposed model selection criteria are based on philosophy of science theories . the proposed model is based upon a model that is more explainable than a classical model . |
Have a Cake and Eat it Too: Assessing Discriminating Performance of an Intelligibility Index Obtained from a Reduced Sample Size (2020.lrec-1)
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| Challenge: | Random vs. phonetically motivated reduction of linguistic material in speech disordered populations has a higher ranking ability than when the sample size is reduced based on a phonetic criterium. |
| Approach: | They investigate random vs. phonetically motivated reduction of linguistic material in speech disordered populations and their impact on the discrimination classifier. |
| Outcome: | The proposed method is reliable and efficient, and it is of significant interest to clinicians and patients. |
Evaluation Metrics for Headline Generation Using Deep Pre-Trained Embeddings (2020.lrec-1)
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| Challenge: | Recent generative language models have shown promise in abstractive summarization tasks. |
| Approach: | They propose to use Fr echet embedding distance and angular embeddable similarity to evaluate the performance of generative language models in abstractive summarization tasks. |
| Outcome: | The proposed metric shows close relation with human judgments and has overall better correlations with them. |
LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation (2020.lrec-1)
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| Challenge: | Recent trends in NLP research have raised an interest in linguistic code-switching . however, many of these approaches are limited to a few language pairs and a specific domain . |
| Approach: | They propose a centralized benchmark for Linguistic Code-switching Evaluation that combines eleven corpora covering four different code-switch languages and four tasks. |
| Outcome: | The proposed benchmark provides a centralized benchmark and compares with other benchmarks in real-time. |
Paraphrase Generation and Evaluation on Colloquial-Style Sentences (2020.lrec-1)
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| Challenge: | a new study investigates the quality and novelty of generated paraphrases . paraphrase models can be used for information retrieval and data mining . |
| Approach: | They use state-of-the-art neural machine translation models trained on the Opusparcus corpus to generate paraphrases in six languages. |
| Outcome: | The proposed model outperforms the existing model on human evaluation in five of the six languages. |
Analyzing Word Embedding Through Structural Equation Modeling (2020.lrec-1)
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| Challenge: | Existing studies have shown that word embedding improves accuracy on NLP tasks. |
| Approach: | They propose a causal diagram based on the evaluation results of word embeddings using partial least squares path modeling. |
| Outcome: | The proposed model proves that word embedding contributes to solving downstream tasks. |
Evaluation of Lifelong Learning Systems (2020.lrec-1)
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| Challenge: | Current intelligent systems need the expensive support of machine learning experts to sustain their performance level when used on a daily basis. |
| Approach: | They propose a generic evaluation methodology for lifelong learning systems . they use "initialisation data" to refer to the set of training, development and test data together . |
| Outcome: | The proposed evaluation method is based on the evaluation of human-assisted learning outside the context of lifelong learning. |
Interannotator Agreement for Lexico-Semantic Annotation of a Corpus (2020.lrec-1)
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| Challenge: | a method for lexico-semantic annotation of the Basic Corpus of Polish Metaphors is described . the procedure is composed of three steps: deciding whether a particular occurrence of a word is asemantics or strictly grammatical. |
| Approach: | They propose a procedure for lexico-semantic annotation of the Basic Corpus of Polish Metaphor . procedure corrects morphosyntactic annotation of part of corpus that is automatically annotated . |
| Outcome: | The proposed procedure corrects the morphosyntactic annotation of part of the corpus . it is composed of three steps: deciding whether a word is asemantic or strictly grammatical . preliminary results show that the procedure is adequate for the task . |
An In-Depth Comparison of 14 Spelling Correction Tools on a Common Benchmark (2020.lrec-1)
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| Challenge: | False positives and false negatives are common spelling and grammar errors. |
| Approach: | They evaluate 14 spelling correction tools on a common benchmark . they compare sentences from the English Wikipedia distorted using a realistic error model . |
| Outcome: | The evaluation provides a detailed comparison with respect to 12 error categories. |
Sentence Level Human Translation Quality Estimation with Attention-based Neural Networks (2020.lrec-1)
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| Challenge: | Existing methods for assessing translation quality rely on manual features and external knowledge. |
| Approach: | They propose to use a neural model without feature engineering to detect which parts in sentence pairs are most relevant for assessing quality. |
| Outcome: | The proposed model outperforms feature-based methods on a large human annotated dataset. |
Evaluating Language Tools for Fifteen EU-official Under-resourced Languages (2020.lrec-1)
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| Challenge: | Evaluation of language tools available for 15 EU-official under-resourced languages . evaluation of NERC systems was problematic because of lack of universally or cross-lingually applicable named entities classification scheme. |
| Approach: | They evaluate language tools available for 15 EU-official under-resourced languages . they focus on existing NLP platforms that provide models for under-represented languages - stanton core, nl cube, uDPipe . |
| Outcome: | The evaluation of language tools for 15 under-resourced languages is reproducible . the results are below what was reported in the literature and in some cases even better than the ones reported previously. |
Word Embedding Evaluation for Sinhala (2020.lrec-1)
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| Challenge: | Word embeddings are a key component of the natural language processing process. |
| Approach: | They evaluate three standard word embedding models for Sinhala language using two evaluation methods: intrinsic evaluation and extrinsic evaluation. |
| Outcome: | The proposed models performed best in the three evaluation tasks, while FastText and Glove showed the lowest accuracies. |
Stress Test Evaluation of Transformer-based Models in Natural Language Understanding Tasks (2020.lrec-1)
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| Challenge: | Existing models are weak and take advantage of failures and errors in datasets to improve performance. |
| Approach: | They evaluate three Transformer-based models in Natural Language Inference and Question Answering tasks to see if they are more robust or have the same flaws as their predecessors. |
| Outcome: | The proposed models outperform recurrent neural network models to stress tests on both NLI and QA tasks. |
Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations (2020.lrec-1)
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| Challenge: | a study of the performance of NLP in relation extraction focuses on a business sector . a morphological dictionary can be used to extract named-entity pairs . |
| Approach: | They propose to use annotated textual corpora to perform Brand-Product relation extraction . they propose to propose query expansion by morpho-syntactically related words . |
| Outcome: | The proposed method improves the performance of the Brand-Product relation extraction task. |
A Tale of Three Parsers: Towards Diagnostic Evaluation for Meaning Representation Parsing (2020.lrec-1)
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| Challenge: | Empirical results suggest that the proposed methodology can be meaningfully applied to parsing into graph-structured target representations, uncovering hitherto unknown properties of the different approaches. |
| Approach: | They propose to map from natural language utterances to graph-based encodings of its semantic structure using contrastive and diagnostic evaluation techniques. |
| Outcome: | The proposed method can be meaningfully applied to parsing into graph-structured target representations, uncovering hitherto unknown properties of the different systems that can inform future development and cross-fertilization across approaches. |
Headword-Oriented Entity Linking: A Special Entity Linking Task with Dataset and Baseline (2020.lrec-1)
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| Challenge: | specialized entity linking problem involves linking only headwords of entities to knowledge bases . full product names are rarely written in context, instead abbreviated to shorter, irregular versions . |
| Approach: | They propose a specialized entity linking problem where only the headwords of entities are to be linked to knowledge bases. |
| Outcome: | The proposed model provides a strong benchmark performance on the special task. |
TableBank: Table Benchmark for Image-based Table Detection and Recognition (2020.lrec-1)
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| Challenge: | Existing techniques for table detection and recognition are limited to document types and layouts. |
| Approach: | They propose to build a table detection and recognition dataset with weak supervision from Word and Latex documents on the internet. |
| Outcome: | The proposed dataset contains 417K high quality labeled tables and is publicly available. |
WIKIR: A Python Toolkit for Building a Large-scale Wikipedia-based English Information Retrieval Dataset (2020.lrec-1)
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| Challenge: | ad-hoc information retrieval methods usually require large amounts of annotated data to be effective. |
| Approach: | They propose an open-source toolkit to automatically build large-scale English information retrieval datasets based on Wikipedia. |
| Outcome: | The proposed toolkit builds large-scale English information retrieval datasets based on Wikipedia with 59,252 queries and 2,617,003 pairs. |
Constructing a Public Meeting Corpus (2020.lrec-1)
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Koji Tanaka, Chenhui Chu, Haolin Ren, Benjamin Renoust, Yuta Nakashima, Noriko Takemura, Hajime Nagahara, Takao Fujikawa
| Challenge: | Existing corpora are created from text that has already been digitized. |
| Approach: | They propose a full pipeline of analysis of a large corpus about a century of public meeting in historical Australian news papers, from construction to visual exploration. |
| Outcome: | The proposed method achieves a high recall rate and an F-score of 87.8% on a historical Australian newspaper database. |
Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature (2020.lrec-1)
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| Challenge: | synthesis process is essential for computational experiment design in the field of inorganic materials chemistry. |
| Approach: | They propose a corpus of the synthesis process for all-solid-state batteries and an automated machine reading system for extracting the buried synthesis processes. |
| Outcome: | The proposed system can detect entities with a macro-averaged F1 score of 0.826 while the rule-based relation extractor can achieve high performance. |
WEXEA: Wikipedia EXhaustive Entity Annotation (2020.lrec-1)
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| Challenge: | Existing methods for extracting factual knowledge from text are limited to a few subtasks. |
| Approach: | They propose to use Wikipedia to build a corpus with exhaustive annotations of entity mentions. |
| Outcome: | The proposed system can be used to build supervised datasets and can be reproduced by everyone. |
Handling Entity Normalization with no Annotated Corpus: Weakly Supervised Methods Based on Distributional Representation and Ontological Information (2020.lrec-1)
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Arnaud Ferré, Robert Bossy, Mouhamadou Ba, Louise Deléger, Thomas Lavergne, Pierre Zweigenbaum, Claire Nédellec
| Challenge: | Entity normalization is an important subtask of information extraction . it links entities mentions in text to categories or concepts in a reference vocabulary . |
| Approach: | They propose a method that uses corpus selection, pre-processing and weak supervision strategies to address the scarcity of training data. |
| Outcome: | The proposed method outperforms state-of-the-art methods in terms of accuracy and parametrization . it uses corpus selection, pre-processing and weak supervision strategies . |
HBCP Corpus: A New Resource for the Analysis of Behavioural Change Intervention Reports (2020.lrec-1)
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Francesca Bonin, Martin Gleize, Ailbhe Finnerty, Candice Moore, Charles Jochim, Emma Norris, Yufang Hou, Alison J. Wright, Debasis Ganguly, Emily Hayes, Silje Zink, Alessandra Pascale, Pol Mac Aonghusa, Susan Michie
| Challenge: | Automated extraction of the reports’ intervention content, population, settings and their results is essential in synthesising and summarising the literature. |
| Approach: | They propose to build a corpus of published behaviour change intervention evaluation reports aimed at smoking cessation and to release an annotation dataset. |
| Outcome: | The proposed corpus and annotation dataset are being made available to the community. |
Cross-lingual Structure Transfer for Zero-resource Event Extraction (2020.lrec-1)
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| Challenge: | Existing approaches for information extraction only use name tagging . Currently, most successful cross-lingual transfer learning methods are limited to sequence labeling . |
| Approach: | They propose a share-and-transfer framework to transfer graph structures across languages . they propose to convert sentences in any language to language-universal graph structures . |
| Outcome: | The proposed framework performs comparable to state-of-the-art models on three languages without annotations. |
Cross-Domain Evaluation of Edge Detection for Biomedical Event Extraction (2020.lrec-1)
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| Challenge: | Biomedical event extraction systems are evaluated in-domain and on complete event structures only. |
| Approach: | They present a cross-domain study of edge detection for biomedical event extraction . they analyze differences between five existing gold standard corpora and provide a strong baseline model . |
| Outcome: | The proposed model shows a drop in performance when the baseline is applied on out-of-domain data. |
Semantic Annotation for Improved Safety in Construction Work (2020.lrec-1)
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| Challenge: | a number of documents provide evidence of previous incidents and mitigation strategies . but information about previous projects with similar attributes is often hidden within . a new named entity annotation scheme is being developed for construction safety . |
| Approach: | a team of four health and safety experts have developed a named entity annotation scheme for construction safety documents. |
| Outcome: | a new named entity annotation scheme annotates 600 sentences from accident reports . the scheme has an average agreement rate of 0.79 F-Score . |
Social Web Observatory: A Platform and Method for Gathering Knowledge on Entities from Different Textual Sources (2020.lrec-1)
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| Challenge: | a framework for gathering entity-centered information is needed in real-life scenarios . a social web observatory system allows users to define their own entities . |
| Approach: | They propose a framework for the collection and summarization of information from the Web in an entity-driven manner. |
| Outcome: | The proposed framework is based on a language analysis pipeline and a human user study. |
Development of a Corpus Annotated with Medications and their Attributes in Psychiatric Health Records (2020.lrec-1)
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Jaya Chaturvedi, Natalia Viani, Jyoti Sanyal, Chloe Tytherleigh, Idil Hasan, Kate Baird, Sumithra Velupillai, Robert Stewart, Angus Roberts
| Challenge: | Free text fields within electronic health records (EHRs) contain valuable clinical information which is often missed when conducting research using EHR databases. |
| Approach: | They propose to extract medication annotations from mental health records by including contextual information around them. |
| Outcome: | The aim of the study is to provide a more complete picture behind the mention of medications in the health records, by including additional contextual information around them. |
Do not let the history haunt you: Mitigating Compounding Errors in Conversational Question Answering (2020.lrec-1)
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| Challenge: | Existing approaches employ human-written ground-truth answers for answering conversational questions at test time, but in a realistic scenario, the CoQA model will not have access to ground-Truth answers. |
| Approach: | They propose a sampling strategy that dynamically selects between target answers and model predictions during training, closely simulating the situation at test time. |
| Outcome: | The proposed sampling strategy closely simulates the situation at test time and significantly lowers the performance of CoQA systems. |
CLEEK: A Chinese Long-text Corpus for Entity Linking (2020.lrec-1)
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| Challenge: | Entity linking is a fundamental task in natural language processing, says nigel kilgstrom . existing corpora for entity linking in china are lacking and deficient, he says . kilsmstrom: a new method for entity disambiguation can be developed for Chinese . |
| Approach: | They build a Chinese corpus of multi-domain long text for entity linking . they evaluate the difficulty of documents with respect to entity linking using a measure . |
| Outcome: | The proposed corpus is based on 100 documents from diverse domains and is publicly accessible. |
The Medical Scribe: Corpus Development and Model Performance Analyses (2020.lrec-1)
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Izhak Shafran, Nan Du, Linh Tran, Amanda Perry, Lauren Keyes, Mark Knichel, Ashley Domin, Lei Huang, Yu-hui Chen, Gang Li, Mingqiu Wang, Laurent El Shafey, Hagen Soltau, Justin Stuart Paul
| Challenge: | Existing tools to assist in clinical note generation using audio of provider-patient encounters are lacking. |
| Approach: | They develop an annotation scheme to extract relevant clinical concepts from audio of provider-patient encounters and train a state-of-the-art tagging model. |
| Outcome: | The proposed model is more useful than the F-scores reflect and can be used in clinical notes. |
A Contract Corpus for Recognizing Rights and Obligations (2020.lrec-1)
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| Challenge: | Understanding the content of a contract is often difficult and costly, especially if the contract is long and complex. |
| Approach: | They describe how they built an annotated corpus of contract documents that can be used to recognize rights and obligations. |
| Outcome: | The proposed system can recognize parties' rights and obligations based on 46 English contracts and 25 Japanese contracts drafted by lawyers. |
Recognition of Implicit Geographic Movement in Text (2020.lrec-1)
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| Challenge: | a growing field of research is analyzing the geographic movement of humans, animals, and other entities. |
| Approach: | They created a corpus of sentences labeled as describing geographic movement or not . they used hand labeling, crowd voting and machine learning to predict more labels . |
| Outcome: | a new method uses hand labeling, crowd voting and machine learning to predict more labels. |
Extraction of the Argument Structure of Tokyo Metropolitan Assembly Minutes: Segmentation of Question-and-Answer Sets (2020.lrec-1)
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Keiichi Takamaru, Yasutomo Kimura, Hideyuki Shibuki, Hokuto Ototake, Yuzu Uchida, Kotaro Sakamoto, Madoka Ishioroshi, Teruko Mitamura, Noriko Kando
| Challenge: | a study analyzed local assembly minutes in Japan using a unified format . local assembly minute data is expensive to analyze because of the different ways they are released to the public. |
| Approach: | They construct a corpus of Japanese local assembly minutes based on local autonomy law . they structured all statements in assembly minutes and extracted question and answer pairs . |
| Outcome: | The results show that the minutes are the primary information for local politics. |
A Term Extraction Approach to Survey Analysis in Health Care (2020.lrec-1)
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| Challenge: | a new study examines the impact of customer feedback on health care organizations . the results of the 2017 Irish National Inpatient Survey are compared to a manual framework . |
| Approach: | They propose an approach to patient experience using free text questions from the 2017 Irish National Inpatient Survey campaign. |
| Outcome: | The proposed approach to patient experience is based on the results of the 2017 Irish National Inpatient Survey. |
A Scientific Information Extraction Dataset for Nature Inspired Engineering (2020.lrec-1)
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| Challenge: | Existing approaches to extract relevant biological information from scientific literature are difficult and require domain-specific knowledge. |
| Approach: | They describe a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text. |
| Outcome: | The proposed dataset allows for training and evaluation of Relation Extraction algorithms that aim for coarse-grained typing of scientific biological documents, enabling a high-level filter for engineers. |
Automated Discovery of Mathematical Definitions in Text (2020.lrec-1)
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| Challenge: | a recent study shows that definition extraction is inefficient for one-sentence definitions . definitions are used in many automatic text analysis tasks, including ontology matching and construction . |
| Approach: | They propose to use convolutional neural network and recurrent neural network to identify mathematical definitions from one sentence. |
| Outcome: | The proposed dataset shows that deep learning methods can identify definitions from mathematical texts. |
WN-Salience: A Corpus of News Articles with Entity Salience Annotations (2020.lrec-1)
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| Challenge: | Existing work on entity salience does not distinguish between salient and non-salient entities. |
| Approach: | They propose a dataset to measure entity salience using WikiNews dataset . WN-Salience is built on top of Wikinews, a Wikimedia project . |
| Outcome: | The proposed dataset can be used to benchmark tasks such as entity salience detection and salient entity linking. |
Event Extraction from Unstructured Amharic Text (2020.lrec-1)
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| Challenge: | Existing information extraction systems for Amharic have not represented the linguistic structure and morphological richness of the languages. |
| Approach: | They propose a system that extracts an event from unstructured Amharic text using supervised machine learning and rule-based approaches. |
| Outcome: | The proposed system outperforms the existing rule-based method on Amharic text. |
Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language (2020.lrec-1)
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| Challenge: | Using available datasets, we compare deep learning and traditional machine learning methods for various NLP tasks in Italian. |
| Approach: | They compare deep learning and traditional machine learning methods for various NLP tasks in Italian. |
| Outcome: | The proposed methods outperform traditional methods in sequence tagging tasks and classification tasks in Italian. |
MyFixit: An Annotated Dataset, Annotation Tool, and Baseline Methods for Information Extraction from Repair Manuals (2020.lrec-1)
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| Challenge: | Textual instructions are among the most widely used media for learning and teaching . however, to create autonomous systems, it is difficult to extract task knowledge from text . |
| Approach: | They propose methods that can extract information from repair manuals from a semi-structured dataset . they propose a bag-of-n-grams similarity method and deep-learning-based sequence labeling model . |
| Outcome: | The proposed methods can extract the needed tools and disassembled parts from repair manuals. |
Towards Entity Spaces (2020.lrec-1)
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| Challenge: | Entities are a central element of knowledge bases and are used in many knowledge-centric tasks including text analysis. |
| Approach: | They propose to use entity spaces to represent a set of associated entities with near-identity to provide a handle to an amorphous grouping of entities. |
| Outcome: | The proposed representations improve recall of entity linking in English by using disambiguation pages. |
Love Me, Love Me, Say (and Write!) that You Love Me: Enriching the WASABI Song Corpus with Lyrics Annotations (2020.lrec-1)
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| Challenge: | a corpus of songs enriched with metadata extracted from music databases on the Web contains 1.73M songs with lyrics (1.41M unique lyrics) a researcher proposes methods to extract relevant information from lyrics, including their structure segmentation, topic, explicitness of lyrics content, salient passages of a song and emotions conveyed. |
| Approach: | They propose to extract relevant information from lyrics by using music databases . they propose to use metadata extracted from music databases to analyze lyrics . |
| Outcome: | The proposed methods can be exploited by music search engines and music professionals to better handle large collections of lyrics. |
Evaluating Information Loss in Temporal Dependency Trees (2020.lrec-1)
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| Challenge: | Temporal Dependency Trees (TDTs) are an alternative to full temporal graphs for representing the temporal structure of texts. |
| Approach: | They propose a method to quantify temporal indeterminacy using temporal constraint problems to extract timelines from temporal graphs. |
| Outcome: | The proposed method shows that the tree form of TDTs results in a 109% increase in temporal indeterminacy over their corresponding temporal graphs. |
Populating Legal Ontologies using Semantic Role Labeling (2020.lrec-1)
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Llio Humphreys, Guido Boella, Luigi Di Caro, Livio Robaldo, Leon van der Torre, Sepideh Ghanavati, Robert Muthuri
| Challenge: | This paper is concerned with the ‘resource consumption bottleneck’ of creating semantic technologies manually. |
| Approach: | They propose to combine general-purpose NLP modules with pre- and post-processing using rules based on domain knowledge to solve the acquisition paradox. |
| Outcome: | The proposed system extracts norms from legislation and represents them as structured norms in legal ontologies. |
PST 2.0 – Corpus of Polish Spatial Texts (2020.lrec-1)
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| Challenge: | In this paper, we focus on modeling spatial expressions in texts. |
| Approach: | They propose guidelines for annotating the PST 2.0 corpus of Polish Spatial Texts based on existing standards for English and discuss modifications to the guidelines to the characteristics of the language. |
| Outcome: | The proposed framework is based on three existing standards for English and ISO-Space1.4 from SpaceEval 2014 . |
Natural Language Premise Selection: Finding Supporting Statements for Mathematical Text (2020.lrec-1)
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| Challenge: | Existing approaches to understand mathematical discourse are limited by the complexity of word and symbol interactions. |
| Approach: | They propose a task to retrieve supporting definitions and supporting propositions from a dataset that can be used to evaluate different approaches for the task. |
| Outcome: | The proposed task is based on a dataset that can be used to evaluate different approaches for the natural premise selection task. |
Odinson: A Fast Rule-based Information Extraction Framework (2020.lrec-1)
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| Challenge: | Odinson is a rule-based information extraction framework that matches over multiple representations of text in near real time. |
| Approach: | They propose a rule-based information extraction framework that matches patterns over multiple representations of text with a runtime system that operates in near real time. |
| Outcome: | The proposed framework matches a graph traversal in 2.8 seconds in a corpus of over 134 million sentences, nearly 150,000 times faster than its predecessor. |
The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources (2020.lrec-1)
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| Challenge: | a dataset for scientific entity extraction, classification, and resolution has been developed . a generic conceptual formalism for scientific entities is feasible, the authors say . |
| Approach: | They propose a STEM-ECR dataset that provides a domain-independent benchmark for scientific entity extraction, classification, and resolution tasks. |
| Outcome: | The proposed dataset provides a benchmark for evaluation of scientific entity extraction, classification, and resolution tasks in a domain-independent fashion. |
MathAlign: Linking Formula Identifiers to their Contextual Natural Language Descriptions (2020.lrec-1)
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Maria Alexeeva, Rebecca Sharp, Marco A. Valenzuela-Escárcega, Jennifer Kadowaki, Adarsh Pyarelal, Clayton Morrison
| Challenge: | Existing approaches to extract mathematical concepts and their descriptions are useful for a variety of tasks, including math information retrieval and accessibility efforts to make scientific documents available to the visually impaired. |
| Approach: | They propose a rule-based approach which extracts LaTeX representations of formula identifiers and links them to their in-text descriptions, given only the original PDF and the location of the formula of interest. |
| Outcome: | The proposed approach extracts LaTeX representations of formula identifiers and links them to their in-text descriptions, given only the original PDF and the location of the formula of interest. |
Domain Adapted Distant Supervision for Pedagogically Motivated Relation Extraction (2020.lrec-1)
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| Challenge: | Existing systems for question generation based on shallow linguistic information at text level generate questions for reading comprehension. |
| Approach: | They propose a relation extraction system that extracts pedagogically motivated relation types from a text to generate questions for reading comprehension. |
| Outcome: | The proposed system extracts pedagogically motivated relations from a text . it performs better on the subset of relations suitable for pedagical purposes. |
Temporal Histories of Epidemic Events (THEE): A Case Study in Temporal Annotation for Public Health (2020.lrec-1)
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| Challenge: | Current EBS estimates the occurrence time of events based on coarse metadata such as document publication time. |
| Approach: | They propose a temporal annotation standard THEE-TimeML and a corpus TheeBank . they document the corpus annotation process and demonstrate the immediate benefit . |
| Outcome: | The proposed standards are based on the existing timeML and the corpus TheeBank . the proposed standards demonstrate the immediate benefit to public health applications . |
Exploiting Citation Knowledge in Personalised Recommendation of Recent Scientific Publications (2020.lrec-1)
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| Challenge: | Keeping up with the most recent scientific literature is a challenge for many researchers given the continuous and increasing growth of academic publications. |
| Approach: | They propose to use citation knowledge to provide personalised recommendations of recent scientific publications to a particular user by capturing authors’ publication history and enriched with different forms of paper citation. |
| Outcome: | The proposed dataset captures authors’ publication history and is enriched with different forms of paper citation knowledge, namely citation graphs, citation positions, cited contexts, and citation types. |
A Platform for Event Extraction in Hindi (2020.lrec-1)
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| Challenge: | Event Extraction is an important task in the widespread field of NLP, but there is no benchmark setup in Hindi. |
| Approach: | They propose an Event Extraction framework for Hindi language and develop deep learning based models to set as the baselines. |
| Outcome: | The proposed framework crawls more than seventeen hundred disaster related Hindi news articles from various news sources. |
Rad-SpatialNet: A Frame-based Resource for Fine-Grained Spatial Relations in Radiology Reports (2020.lrec-1)
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Surabhi Datta, Morgan Ulinski, Jordan Godfrey-Stovall, Shekhar Khanpara, Roy F. Riascos-Castaneda, Kirk Roberts
| Challenge: | Existing framework for encoding spatial language in radiology is based on frame semantics . |
| Approach: | They propose a framework for encoding spatial language in radiology based on frame semantics and a corpus of 400 radiology reports annotated with spatial trigger expressions and contextual information. |
| Outcome: | The proposed framework is based on the existing SpatialNet representation in the general domain and is able to generate more accurate representations of spatial language in radiology. |
NLP Analytics in Finance with DoRe: A French 250M Tokens Corpus of Corporate Annual Reports (2020.lrec-1)
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| Challenge: | Recent advances in neural computing and word embeddings for semantic processing open many new applications areas which had been left unaddressed due to inadequate language understanding capacity. |
| Approach: | They propose a French and dialectal French corpus for NLP analytics in finance, regulation and investment. |
| Outcome: | The proposed corpus is designed to be as modular as possible to allow for maximum reuse in different tasks pertaining to Economics, Finance and Investment. |
The Language of Brain Signals: Natural Language Processing of Electroencephalography Reports (2020.lrec-1)
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| Challenge: | Clinical electroencephalography (EEG) is an excellent tool for probing neural function. |
| Approach: | They propose to use EEG to capture brain signals and its correlations with pathologies by a corpus of EEG reports to provide examples of EMG-specific concepts. |
| Outcome: | The proposed method provides examples of EEG-specific and clinically relevant concepts and exemplifies a self-attention joint-learning model to predict similar annotations in the EEG report corpus. |
Humans Keep It One Hundred: an Overview of AI Journey (2020.lrec-1)
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Tatiana Shavrina, Anton Emelyanov, Alena Fenogenova, Vadim Fomin, Vladislav Mikhailov, Andrey Evlampiev, Valentin Malykh, Vladimir Larin, Alex Natekin, Aleksandr Vatulin, Peter Romov, Daniil Anastasiev, Nikolai Zinov, Andrey Chertok
| Challenge: | Artificial General Intelligence (AGI) is showing growing performance in numerous applications - beating human performance in Chess and Go, using knowledge bases and text sources to answer questions and even pass human examination. |
| Approach: | They propose to use knowledge bases and text sources to answer questions to improve AI performance on knowledge bases, reasoning and text generation. |
| Outcome: | The proposed AI Journey system passed the final native language exam in Russian with a high score of 69%, with 68% being an average human result. |
Towards Data-driven Ontologies: a Filtering Approach using Keywords and Natural Language Constructs (2020.lrec-1)
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| Challenge: | Creating ontologies is an expensive task. |
| Approach: | They evaluate two commonly used methods, OpenIE and co-occurrences, and use them to generate ontologies from documents. |
| Outcome: | The proposed methods perform better on pizza and agriculture document sets than OpenIE and co-occurrences. |
A French Corpus and Annotation Schema for Named Entity Recognition and Relation Extraction of Financial News (2020.lrec-1)
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| Challenge: | Strict regulatory regimes mandate financial institutions to rigorously monitor their customers' financial activities. |
| Approach: | They propose to use an ontology of compliance-related concepts and relationships along with a corpus annotated according to it to train and evaluate named entity recognition algorithms. |
| Outcome: | The proposed ontology allows for training and evaluating domain-specific named entity recognition and relation extraction algorithms. |
Inferences for Lexical Semantic Resource Building with Less Supervision (2020.lrec-1)
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| Challenge: | lexical semantic resources may be built using various approaches such as extraction from corpora, integration of relevant pieces of knowledge from pre-existing knowledge resources and endogenous inference. |
| Approach: | They propose a method where the resource building process appears as a self learning process . they propose lexical and semantic resource building based on inference . |
| Outcome: | The proposed method reduces the human effort needed for lexical semantic resource building. |
Acquiring Social Knowledge about Personality and Driving-related Behavior (2020.lrec-1)
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| Challenge: | Using crowdsourcing, we acquire human-specific knowledge about personality and driving. |
| Approach: | They propose a psychological approach to collect human-specific social knowledge from a text corpus using NLP techniques. |
| Outcome: | The proposed approach collects human-specific social knowledge from a text corpus, and then implements it into a system. |
Implicit Knowledge in Argumentative Texts: An Annotated Corpus (2020.lrec-1)
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| Challenge: | Especially in argumentative texts, people omit information that seems clear and evident . a computational system typically does not possess commonsense or domain-specific knowledge to reconstruct implied information. |
| Approach: | They build a corpus of human annotations of missing and implied information in argumentative texts. |
| Outcome: | The proposed dataset can help to assist automated argument analysis and guide the process of revealing implicit information in argumentative texts automatically. |
Multiple Knowledge GraphDB (MKGDB) (2020.lrec-1)
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| Challenge: | ConceptNet, DBpedia, WebIsAGraph, WordNet and Wikipedia category hierarchy are used to create a large-scale graph database. |
| Approach: | They propose to use multiple taxonomy backbones extracted from 5 existing knowledge graphs to create a large-scale graph database. |
| Outcome: | The proposed database is intended to favour and support the development of open-domain natural language processing applications relying on knowledge bases. |
Orchestrating NLP Services for the Legal Domain (2020.lrec-1)
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Julian Moreno-Schneider, Georg Rehm, Elena Montiel-Ponsoda, Víctor Rodriguez-Doncel, Artem Revenko, Sotirios Karampatakis, Maria Khvalchik, Christian Sageder, Jorge Gracia, Filippo Maganza
| Challenge: | a legal technology system under development in the EU is based on semantic services and a multilingual legal knowledge Graph. |
| Approach: | They propose a workflow manager that enables flexible orchestration of workflows . they describe different use cases and propose prototypical solutions . |
| Outcome: | The proposed system is based on a set of natural language processing and document curation services and a multilingual legal knowledge graph that contains semantic information and meaningful references to legal documents. |
Evaluation Dataset and Methodology for Extracting Application-Specific Taxonomies from the Wikipedia Knowledge Graph (2020.lrec-1)
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| Challenge: | Recent efforts to extract hierarchical relations from unstructured text have been challenging. |
| Approach: | They propose an iterative method to extract an application-specific gold standard dataset from a Wikipedia knowledge graph and an evaluation framework to assess the quality of noisy automatically extracted taxonomies. |
| Outcome: | The proposed method reduces manual work and provides a first gold standard dataset and evaluation framework. |
Subjective Evaluation of Comprehensibility in Movie Interactions (2020.lrec-1)
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| Challenge: | Various studies have dealt with the comprehensibility of textual, audio, or audiovisual documents. |
| Approach: | They aim to build a corpus of human annotations that could help to study human perceptions of comprehensibility of audiovisual documents. |
| Outcome: | The proposed corpus of human annotations will help to study human perceptions of comprehensibility of audiovisual documents. |
Representing Multiword Term Variation in a Terminological Knowledge Base: a Corpus-Based Study (2020.lrec-1)
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| Challenge: | Multiword terms are the most frequent type of lexical units in scientific and technical communication. rendering them in another language is not easy due to their cognitive complexity, proliferation of different forms, and their unsystematic representation in terminographic resources. |
| Approach: | They evaluated Spanish translation variants of multiword terms in three parallel corpora, two comparable corporales and two terminological resources. |
| Outcome: | The results show that multiword terms exhibit a significant degree of term variation . the proposed model is based on a set of criteria for determining which variants should be selected . |
Understanding Spatial Relations through Multiple Modalities (2020.lrec-1)
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| Challenge: | Existing work on common sense reasoning and understanding of spatial relations is limited. |
| Approach: | They propose a spatial model that uses both textual and visual information to predict spatial relations between two entities in an image. |
| Outcome: | The proposed model improves prediction accuracy and coverage and deals with unseen subjects, objects and relations. |
A Topic-Aligned Multilingual Corpus of Wikipedia Articles for Studying Information Asymmetry in Low Resource Languages (2020.lrec-1)
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| Challenge: | a systematic comparison of information coverage in English Wikipedia and Wikipedias in eight other widely spoken languages is needed to bridge the information gap. |
| Approach: | They compare information coverage in English Wikipedia and Wikipedias in eight other widely spoken languages. |
| Outcome: | The analysis quantifies and provides useful insights about the information gap that exists between different language editions of Wikipedia and offers a roadmap for the IR community to bridge this gap. |
Pártélet: A Hungarian Corpus of Propaganda Texts from the Hungarian Socialist Era (2020.lrec-1)
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| Challenge: | a digitized corpus of Communist propaganda texts is presented in this paper . it represents the direct political agitation and propaganda of the dictatorial system . |
| Approach: | They present a digitized Hungarian corpus of Communist propaganda texts . they use a database to compile a large database of articles from the journal . |
| Outcome: | The proposed dataset provides a unique opportunity for conducting research on Hungarian propaganda discourse . it also provides enables analysis of changes in the political discourse over a 35-year period . |
KORE 50ˆDYWC: An Evaluation Data Set for Entity Linking Based on DBpedia, YAGO, Wikidata, and Crunchbase (2020.lrec-1)
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| Challenge: | A major domain of research in natural language processing is named entity recognition and disambiguation (NERD). |
| Approach: | They extend a widely-used data set to include NERD tasks for DBpedia and YAGO, Wikidata and Crunchbase. |
| Outcome: | The extended data set allows for a broader spectrum of evaluation. |
Eye4Ref: A Multimodal Eye Movement Dataset of Referentially Complex Situations (2020.lrec-1)
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| Challenge: | Eye4Ref is a rich multimodal dataset of eye-movement recordings from referentially complex situated settings. |
| Approach: | They present a rich multimodal dataset of eye-movement recordings from situated settings . they use linguistic labels, saccadic movement parameters and symbolic knowledge representations . |
| Outcome: | The Eye4Ref dataset is an annotated multimodal dataset from three eyetracking studies on reference resolution and disambiguation tasks in situated settings. |
SiBert: Enhanced Chinese Pre-trained Language Model with Sentence Insertion (2020.lrec-1)
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| Challenge: | Recent studies show that pre-trained models can learn unsupervised language representations by self-supervised tasks on large-scale corpora. |
| Approach: | They propose a pre-training task called Sentence Insertion for Chinese query-passage pairs NLP tasks . they propose 'word segmentation' method to enhance Chinese Bert performance . |
| Outcome: | The proposed task improves Chinese pre-trained models significantly. |
Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset (2020.lrec-1)
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Brian Roark, Lawrence Wolf-Sonkin, Christo Kirov, Sabrina J. Mielke, Cibu Johny, Isin Demirsahin, Keith Hall
| Challenge: | a new resource is available for 12 South Asian languages that use the Latin script for text entry . the Latin-script system is not widely used in South Asian language writing, despite the Latin alphabet . |
| Approach: | They describe the Dakshina dataset, a new resource consisting of text in both the Latin and native scripts for 12 South Asian languages. |
| Outcome: | The Dakshina dataset includes text in both the Latin and native scripts for 12 languages . the authors provide baseline results on several tasks made possible by the dataset . |
GM-RKB WikiText Error Correction Task and Baselines (2020.lrec-1)
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| Challenge: | GM-RKB WikiText Error Correction Task for automatic detection and correction of typographical errors in Wikitext annotated pages. |
| Approach: | They propose to use a GM-RKB semantic wiki to automatically detect typographical errors in WikiText annotated pages. |
| Outcome: | The included corpus is based on a snapshot of the GM-RKB domain-specific semantic wiki consisting of a large collection of concepts, personages, and publications . Numerous Wikipedia pages were also included as additional training data in the task’s evaluation process. |
Embedding Space Correlation as a Measure of Domain Similarity (2020.lrec-1)
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| Challenge: | Existing work on domain similarity using text-based features of corpus is limited by pre-trained word embeddings. |
| Approach: | They propose a measure of domain similarity based on dimension-wise correlations between embedding spaces . they find a threshold at which the measure indicates that two corpora come from the same domain . |
| Outcome: | The proposed measure can be used to determine which corpora are more similar to each other in a cross-domain sentiment detection task. |
Wiki-40B: Multilingual Language Model Dataset (2020.lrec-1)
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| Challenge: | We propose a new multilingual language model benchmark that is composed of 40+ languages spanning several scripts and linguistic families. |
| Approach: | They propose a multilingual language model benchmark composed of 40+ languages . they train monolingual causal language models using a state-of-the-art model . |
| Outcome: | The proposed model is composed of 40+ languages spanning several scripts and linguistic families. |
Know thy Corpus! Robust Methods for Digital Curation of Web corpora (2020.lrec-1)
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| Challenge: | Existing methods for estimating the lexicon of Web corpora have not been used to train pre-trained models. |
| Approach: | They propose a framework for digital curation of Web corpora to provide robust estimation of their parameters. |
| Outcome: | The proposed framework provides robust estimation of Web corpora's composition and lexicon . the proposed framework is similar to the BNC and ELMO models, but lacks curated categories . |
Evaluating Approaches to Personalizing Language Models (2020.lrec-1)
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| Challenge: | a large amount of text is not available for training a user-specific language model, which suggests a need to personalize language models with only a small amount of data. |
| Approach: | They propose three approaches to personalize a language model that was trained on a large background corpus using a relatively small amount of text from an individual user. |
| Outcome: | The proposed techniques outperform language model adaptation based on demographic factors. |
Class-based LSTM Russian Language Model with Linguistic Information (2020.lrec-1)
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| Challenge: | LSTM models can be used in speech recognition systems at N-best or lattice rescoring stage. |
| Approach: | They propose to use word frequency and linguistic information to generate class-based LSTM Russian language models with various numbers of classes. |
| Outcome: | The proposed models outperform word-based models and word2vec models in terms of perplexity, training time, and word error rate. |
Adaptation of Deep Bidirectional Transformers for Afrikaans Language (2020.lrec-1)
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| Challenge: | Pretrained language models can be trained in unsupervised manner, but can be difficult to implement because of the amount of data and computational resources needed for pretraining. |
| Approach: | They propose a model for Afrikaans based on bidirectional encoder representation from transformers. |
| Outcome: | The proposed model outperforms the existing models in part-of-speech tagging, named-entity recognition, and dependency parsing tasks. |
FlauBERT: Unsupervised Language Model Pre-training for French (2020.lrec-1)
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Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoit Crabbé, Laurent Besacier, Didier Schwab
| Challenge: | Language models are a key step to achieve state-of-the-art results in many different Natural Language Processing (NLP) tasks. |
| Approach: | They propose to use a language model that is pre-trained on a large and heterogeneous French corpus to train continuous word representations. |
| Outcome: | The proposed model outperforms existing models on a large and heterogeneous French corpus. |
Accelerated High-Quality Mutual-Information Based Word Clustering (2020.lrec-1)
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| Challenge: | Word clustering is a hard hierarchical clustering that uses short-range distributional information to construct clusters. |
| Approach: | They propose to use a hierarchical clustering algorithm with a fixed-width beam to build clusters that outperform other word representations. |
| Outcome: | The proposed method outperforms the original methods in the computation of hierarchical and flat clusters. |
Rhythmic Proximity Between Natives And Learners Of French - Evaluation of a metric based on the CEFC corpus (2020.lrec-1)
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| Challenge: | Among prosodic parameters, rhythm is one that varies noticeably from one language to another. |
| Approach: | They propose to model rhythm in French using the corpus for l’Étude du Français Contemporain (CEFC) . they tested 146 native speakers, 37 non-native speakers and 29 non-Native Japanese learners of French . |
| Outcome: | The proposed model is based on the corpus pour l’Étude du Français Contemporain (CEFC) which contains up to 300 hours of speech of a wide variety of speaker profiles and situations. |
From Linguistic Resources to Ontology-Aware Terminologies: Minding the Representation Gap (2020.lrec-1)
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| Challenge: | Terminological resources are not available in standard formats such as Term Base eXchange (TBX) thus preventing their sharing and reuse. |
| Approach: | They propose to convert terminological resources into TBX format and to integrate ontology-based information into terminologies. |
| Outcome: | The proposed tool supports the process of creating ontology-aware terminologies . terminologie creation and maintenance determine the quality of the final product of a translation process . |
Modeling Factual Claims with Semantic Frames (2020.lrec-1)
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| Challenge: | In recent years, the proliferation of misinformation has reached a staggering pace eroding people's confidence in politics and even affected democracies. |
| Approach: | They propose an extension of the Berkeley FrameNet for the structured and semantic modeling of factual claims. |
| Outcome: | The proposed extension provides 2,540 fully annotated sentences and can be used to understand how these frames are intended to work and to train machine learning models. |
Automatic Transcription Challenges for Inuktitut, a Low-Resource Polysynthetic Language (2020.lrec-1)
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| Challenge: | Inuktitut is one of the 60 Indigenous languages currently spoken in Canada . polysynthetic languages are often termed agglutinative when their morphemes have clear boundaries and thus are easily segmentable. |
| Approach: | They propose to use a corpus of 23 hours of transcribed oral stories to train automatic speech recognition in Inuktitut. |
| Outcome: | The proposed model shows that Inuktitut displays a much higher degree of polysynthesis than other agglutinative languages like Finnish or Turkish. |
Geographically-Balanced Gigaword Corpora for 50 Language Varieties (2020.lrec-1)
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| Challenge: | Existing corpora of text corpors over-represent inner-circle varieties from the US and UK . this paper uses country-level population demographics to correct implicit geographic and demographic biases . |
| Approach: | They propose to use country-level population demographics to correct geographic biases . they use a population-based sampling technique to remove geographic bias from gigaword corpora . |
| Outcome: | The proposed corpus family removes geographic biases by comparing the population-based sampling with the baseline corpus. |
Data Augmentation using Machine Translation for Fake News Detection in the Urdu Language (2020.lrec-1)
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| Challenge: | supervised machine learning requires substantial amount of annotated data. |
| Approach: | They propose to use machine translation to augment annotated corpora for fake news detection in Urdu . they train a fake news classifier on an annotation dataset originally in Uru . |
| Outcome: | The proposed method fails to improve fake news detection in Urdu at the current state of machine translation quality. |
Evaluation of Greek Word Embeddings (2020.lrec-1)
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| Challenge: | Word embeddings are the most popular input for many NLP tasks. |
| Approach: | They propose to use Greek word embeddings as an unsupervised learning tool . they use a Greek word analogy test set and a morphological test collection to evaluate word similarities . |
| Outcome: | The proposed model is able to create meaningful representations of Greek words . the proposed model can be adapted to Greek language and polysemy . |
A Dataset of Mycenaean Linear B Sequences (2020.lrec-1)
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| Challenge: | a dataset of Mycenaean Linear B sequences is presented . the dataset contains sequences of Mycean words and ideograms according to the rules of the Mycensean Greek language in the Late Bronze Age. |
| Approach: | They propose to collect Mycenaean Linear B sequences from the Mycensean inscriptions . they exploit the structure of the entire language, not just the Mycean vocabulary . |
| Outcome: | The proposed dataset exploits the structure of the entire language, not just the Mycenaean vocabulary, to analyse sequential patterns. |
The Nunavut Hansard Inuktitut–English Parallel Corpus 3.0 with Preliminary Machine Translation Results (2020.lrec-1)
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Eric Joanis, Rebecca Knowles, Roland Kuhn, Samuel Larkin, Patrick Littell, Chi-kiu Lo, Darlene Stewart, Jeffrey Micher
| Challenge: | Inuktitut language is a member of the Inuit-Yupik-Unangan family . it is spoken in two territories, Nunavut and the Northwest Territories . |
| Approach: | They describe a sentence-aligned Inuktitut–English corpus released in Nunavut . it is the largest parallel corpus of a polysynthetic language released to date . they also describe preliminary experiments on machine translation between the languages . |
| Outcome: | The proposed corpus is the largest sentence-aligned corpus of a polysynthetic language or an Indigenous language of the Americas . the alignments were evaluated and the results were compared with other methods . |
Exploring Bilingual Word Embeddings for Hiligaynon, a Low-Resource Language (2020.lrec-1)
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| Challenge: | Existing studies on Hiligaynon, a low-resource language of Malayo-Polynesian origin, have not explored the use of bilingual word embeddings in NLP. |
| Approach: | They use a publicly available Hiligaynon corpus with only 300K words to match it with a comparable English corpus. |
| Outcome: | The proposed model outperforms results from a low-resource language of Malayo-Polynesian origin with over 9 million speakers in the Philippines. |
A Finite-State Morphological Analyser for Evenki (2020.lrec-1)
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| Challenge: | Evenki is a language with rich morphology, therefore a morphological analyser is highly desirable for processing Evenki texts. |
| Approach: | They propose to use a morphological analyser for Evenki to analyze half of the corpus . they evaluate the morphology of available corpora and estimate accuracy, recall and F-score . |
| Outcome: | The proposed morphological analyser can analyse less than a half of the available corpora on Evenki . it is based on the Helsinki Finite-State Transducer toolkit (HFST). |
Morphology-rich Alphasyllabary Embeddings (2020.lrec-1)
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| Challenge: | Word embeddings have been successfully trained in many languages, but evaluations in lesser-resourced languages have been cursory and highly variable. |
| Approach: | They propose to build a word embedding model suitable for the Semitic language of Amharic (Ethiopia) it is morphologically rich and written as an alphasyllabary rather than an alphabet . |
| Outcome: | The proposed model performs on word analogy tasks on the Semitic language of Amharic (Ethiopia) it is morphologically rich and written as an alphasyllabary rather than an alphabet . |
Localization of Fake News Detection via Multitask Transfer Learning (2020.lrec-1)
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| Challenge: | Existing methods for detecting fake news require large labeled datasets and expert-curated corpora, which low-resource languages may not have. |
| Approach: | They construct a benchmark dataset for fake news detection in Filipino using curated corpora and transfer learning techniques. |
| Outcome: | The proposed method can achieve 91% accuracy on a fake news dataset, reducing error by 14% compared to established baselines. |
Evaluating Sentence Segmentation in Different Datasets of Neuropsychological Language Tests in Brazilian Portuguese (2020.lrec-1)
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| Challenge: | Using automated analysis of connected speech is a promising direction for diagnosing cognitive impairments. |
| Approach: | They propose to use a novel model to segment impaired speech transcriptions . they propose to include a Linear Chain CRF and a self-attention mechanism . |
| Outcome: | The proposed system performs better than the existing model with three new datasets used to diagnose cognitive impairments. |
Jejueo Datasets for Machine Translation and Speech Synthesis (2020.lrec-1)
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| Challenge: | Jejueo, or the Jeju language, is a minority language used on Jeju Island . there have been many efforts to revitalize the language, but few computational approaches have been used to solve its problems. |
| Approach: | They construct two new Jejueo datasets using interviews and transcripts . they build machine translation and speech synthesis using these datasets based on their results . |
| Outcome: | The proposed datasets will attract interest of both language and machine learning communities. |
Speech Corpus of Ainu Folklore and End-to-end Speech Recognition for Ainu Language (2020.lrec-1)
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| Challenge: | Ainu is an unwritten language spoken by Ainus, a minority of whom are critically endangered by UNESCO . a project of automatic speech recognition (ASR) for the Ainous language is being developed . |
| Approach: | They propose to use automatic speech recognition for the Ainu language to help preserve its language archives. |
| Outcome: | The proposed system improves word and phone recognition accuracy in speaker-open conditions. |
Development of a Guarani - Spanish Parallel Corpus (2020.lrec-1)
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| Challenge: | a corpus of Guarani sentences with sentence-level alignment is presented . the corpus contains 228,000 Guaran tokens along with 336,000 Spanish tokens . |
| Approach: | They propose to develop a Guarani - Spanish parallel corpus with sentence-level alignment . the corpus contains 228,000 Guaran tokens along with 336,000 Spanish tokens . |
| Outcome: | The proposed corpus contains 22,800 Guarani tokens along with 336,000 Spanish tokens extracted from web sources. |
AR-ASAG An ARabic Dataset for Automatic Short Answer Grading Evaluation (2020.lrec-1)
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| Challenge: | Existing models for short answer grading are limited due to their complexity. |
| Approach: | They propose an Arabic Dataset for automatic short answer grading with a semantic similarity problem between the student's answer and the teacher's model answer. |
| Outcome: | The proposed model is suitable for languages with scarce resources such as Arabic language. |
Processing Language Resources of Under-Resourced and Endangered Languages for the Generation of Augmentative Alternative Communication Boards (2020.lrec-1)
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| Challenge: | Under-resourced and endangered or small languages yield problems for automatic processing and exploiting because of the small amount of available data. |
| Approach: | They propose an approach using enriched linguistic research data to create communication boards commonly used in alternative augmentative communication (AAC) using lexical analysis and rich annotation, the boards can be imported into various AAC software. |
| Outcome: | The proposed approach uses lexical analysis and rich annotations to create communication boards commonly used in alternative augmentative communication (AAC) The created boards can be imported into various AAC software and are available under the CC BY-NC-SA 4.0 (public) license. |
The Nisvai Corpus of Oral Narrative Practices from Malekula (Vanuatu) and its Associated Language Resources (2020.lrec-1)
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| Challenge: | Nisvai is an oral language spoken by 200 native speakers in the south-east of Malekula, Vanuatu. |
| Approach: | They present a corpus of annotated narratives from the Nisvai linguistic community . they use written conventions specifically developed for the language to generate associated resources . |
| Outcome: | The presented corpus is made of 32 annotated narratives segmented into intonation units. the audio records were transcribed using the written conventions specifically developed for the language and translated into French. |
Building a Time-Aligned Cross-Linguistic Reference Corpus from Language Documentation Data (DoReCo) (2020.lrec-1)
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Ludger Paschen, François Delafontaine, Christoph Draxler, Susanne Fuchs, Matthew Stave, Frank Seifart
| Challenge: | Temporal patterns of speech are of central interest in the cognitive sciences as they provide key evidence for the architecture underlying the human language production system. |
| Approach: | They propose to provide time-aligned transcriptions for at least 50 collections of under-resourced languages by standardizing formats and conventions and adding segmental alignments with WebMAUS. |
| Outcome: | The proposed solution bridges the gap between language documentation and linguistic inquiry by providing time-aligned transcriptions for at least 50 collections of under-resourced languages. |
Benchmarking Neural and Statistical Machine Translation on Low-Resource African Languages (2020.lrec-1)
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| Challenge: | a recent study has focused on languages where large amounts of resources are available. |
| Approach: | They benchmark state of the art statistical and neural machine translation systems on Somali and Swahili languages . they find that statistical machine translation and neural translation can perform similarly in low-resource scenarios . |
| Outcome: | The results show that statistical machine translation and neural machine translation perform similarly in low-resource scenarios. |
Improved Finite-State Morphological Analysis for St. Lawrence Island Yupik Using Paradigm Function Morphology (2020.lrec-1)
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| Challenge: | St. Lawrence Island Yupik is an endangered polysynthetic language of the Bering Strait region . linguistic fieldwork observed substantial support within the Yupis for language revitalization . |
| Approach: | They propose a finite-state morphological analyzer for the endangered Yupik language . they use the Paradigm Function Morphology theory of morphology to evaluate the results . |
| Outcome: | The proposed morphological analyzer outperforms existing analyzers in accuracy and coverage rates across multiple datasets. |
Towards a Spell Checker for Zamboanga Chavacano Orthography (2020.lrec-1)
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| Challenge: | Zamboanga Chabacano (ZC) is the most vibrant variety of Philippine Creole Spanish, with over 400,000 native speakers in the country . it is currently the most widely spoken variety of creole in the nation, with 405,798 people claiming it as their mother tongue . |
| Approach: | They propose to use a spelling error taxonomy and an adaptive spell checking approach to correct spelling errors in Zamboanga Chabacano. |
| Outcome: | The proposed spelling error taxonomy and adaptive spell checking approach are suitable for the goals mentioned and could be combined with other current spell checking technologies to achieve even higher performance. |
Identifying Sentiments in Algerian Code-switched User-generated Comments (2020.lrec-1)
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| Challenge: | a recent study has focused on sentiment analysis for the Arabic variety, but it has been extended to other domains. |
| Approach: | They build a corpus of 36,000 code-switched user-generated comments annotated for sentiments in Algerian Arabic. |
| Outcome: | The proposed model performs better on unedited code-switched and unbalanced data across sentiment classes. |
Automatic Creation of Text Corpora for Low-Resource Languages from the Internet: The Case of Swiss German (2020.lrec-1)
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| Challenge: | Despite the small pool of speakers, there are still few natural language processing corpora, studies or tools for Swiss German. |
| Approach: | They propose to use a web scraper to generate the largest Swiss German text corpus . they show that the tool can be applied to other low-resource languages as well . |
| Outcome: | The proposed tool significantly improves language modeling in Swiss German, the authors show . |
Evaluating Sub-word Embeddings in Cross-lingual Models (2020.lrec-1)
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| Challenge: | Existing approaches to learning sub-word embeddings for out-of-vocabulary words have not considered sub- word embedds in cross-lingual models. |
| Approach: | They propose to use sub-word embeddings to form cross-lingual embeddables for out-of-vocabulary (OOV) words for which no embeddibles are available. |
| Outcome: | The proposed bilingual lexicon induction task shows that sub-word embeddings can be leveraged to form cross-lingual embeddables for OOV words. |
A Swiss German Dictionary: Variation in Speech and Writing (2020.lrec-1)
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Larissa Schmidt, Lucy Linder, Sandra Djambazovska, Alexandros Lazaridis, Tanja Samardžić, Claudiu Musat
| Challenge: | Besides standard German, Swiss German is spoken in about two thirds of Switzerland. |
| Approach: | They propose a dictionary containing normalized forms of common Swiss German words paired with Swiss German phonetic transcriptions to alleviate the uncertainty associated with this diversity. |
| Outcome: | The proposed dictionary is the first to combine spontaneous translation and phonetic transcriptions in large-scale, scalable phoneme to grapheme model that generates credible novel Swiss German writings. |
Towards a Corsican Basic Language Resource Kit (2020.lrec-1)
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| Challenge: | a roadmap has been set out for the development of a basic language resource kit for the Corsican language . the goal is to improve the availability of resources and tools for the language based on the Banque de Données Langue Corse project . |
| Approach: | a team of researchers from univ-corse is developing a basic language resource kit for the corsican language . they aim to collect corpora, set up a concordancer, set-up language detection tool, build an electronic dictionary and add a part-of-speech tagger . |
| Outcome: | the goal is to improve the availability of resources and tools for the Corsican language . the roadmap sets out the actions to be undertaken: collection of corpora, setting up of a concordancer, language detection tool, electronic dictionary and part-of-speech tagger. |
Evaluating the Impact of Sub-word Information and Cross-lingual Word Embeddings on Mi’kmaq Language Modelling (2020.lrec-1)
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| Challenge: | Mi'kmaq is an Indigenous language spoken primarily in Eastern Canada. |
| Approach: | They consider n-gram and RNN language models for Mi'kmaq and use them to investigate their performance. |
| Outcome: | The proposed model performs better than word-level models, but does not improve over word-based models. |
Exploring a Choctaw Language Corpus with Word Vectors and Minimum Distance Length (2020.lrec-1)
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| Challenge: | Existing tools to explore low resource languages that require no expert knowledge or substantial labor are limited. |
| Approach: | They introduce additions to the Choctaw corpus by using off-the-shelf tools word2vec and Linguistica to create new computational resources for the American indigenous language. |
| Outcome: | The proposed tools can be implemented with minimal labor in the American indigenous language Choctaw. |
Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yorùbá and Twi (2020.lrec-1)
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| Challenge: | a recent study shows that word embeddings can be useful for training downstream natural language processing tasks. |
| Approach: | They compare word embeddings obtained by word embeds from curated corpora with a language-dependent processing. |
| Outcome: | The proposed model compares word embeddings with word embeds from curated corpora and a language-dependent processing on two African languages. |
TRopBank: Turkish PropBank V2.0 (2020.lrec-1)
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| Challenge: | PropBank is a hand-annotated corpus of propositions used to obtain predicate-argument information of a language. |
| Approach: | They present TRopBank "Turkish PropBank v2.0" which is a hand-annotated corpus of propositions . it is used to obtain the predicate-argument information of a language . |
| Outcome: | The proposed annotations provide the predicate-argument information of a language . the proposed annotation is based on the annotations of 17.673 verbs in Turkish . |
Collection and Annotation of the Romanian Legal Corpus (2020.lrec-1)
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| Challenge: | Currently, the corpus contains more than 140k documents representing the legislative body of Romania. |
| Approach: | They present a Romanian legislative corpus which is a valuable linguistic asset for machine translation systems. |
| Outcome: | The Romanian legislative corpus contains more than 140k documents representing the legislative body of Romania. |
An Empirical Evaluation of Annotation Practices in Corpora from Language Documentation (2020.lrec-1)
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| Challenge: | Language documentation projects have produced substantial amounts of primary data from a wide variety of endangered languages. |
| Approach: | They propose to use common annotation conventions in existing corpora to facilitate their future processing. |
| Outcome: | The proposed formats are based on the common ELAN and Toolbox formats and are used to facilitate their future processing. |
Annotated Corpus for Sentiment Analysis in Odia Language (2020.lrec-1)
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| Challenge: | Existing sentiment analysis models are not available for Odia 1 as it is a resource-poor language. |
| Approach: | They create an annotated Odia corpus and test its usability by training and testing on the corpus using various classifiers. |
| Outcome: | The created corpus contains 2045 Odia sentences from news domain annotated with sentiment labels using a well-defined annotation scheme. |
Building a Task-oriented Dialog System for Languages with no Training Data: the Case for Basque (2020.lrec-1)
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| Challenge: | a new approach to task-oriented dialog systems is proposed for less-resourced languages . the authors compare training data projection with direct model transfer strategies . |
| Approach: | They propose to project existing annotations in rich-resource languages and use NMT to project them. |
| Outcome: | The proposed system outperforms models trained on Basque projected data in intent classification and slot filling tasks. |
SENCORPUS: A French-Wolof Parallel Corpus (2020.lrec-1)
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| Challenge: | Currently, the corpus is being used to develop a neural machine translation model to translate French sentences into Wolof. |
| Approach: | They propose to build a bilingual parallel corpus between French and Wolof, a Niger-Congo language belonging to the Northern branch of the Atlantic group. |
| Outcome: | The proposed corpus contains 70,000 parallel sentences drawn on various sources from different domains. |
A Major Wordnet for a Minority Language: Scottish Gaelic (2020.lrec-1)
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Gábor Bella, Fiona McNeill, Rody Gorman, Caoimhin O Donnaile, Kirsty MacDonald, Yamini Chandrashekar, Abed Alhakim Freihat, Fausto Giunchiglia
| Challenge: | a new wordnet resource is available for Scottish Gaelic, a minority language spoken by 60,000 speakers . weak online presence of minority languages is a problem due to lack of digital corpora, authors say . |
| Approach: | They propose a new wordnet resource for Scottish Gaelic, a Celtic minority language . the wordnet contains over 15 thousand word senses and is among the 30 largest in the world . authors hope to contribute to long-term preservation of Scottish Gaels as a living language - offline and on the Web . |
| Outcome: | The new wordnet is for Scottish Gaelic, a minority language spoken by 60,000 speakers . the wordnet contains over 15 thousand word senses and is among the 30 largest in the world . authors hope it will contribute to the long-term preservation of the language, both offline and on the Web . |
Crowdsourcing Speech Data for Low-Resource Languages from Low-Income Workers (2020.lrec-1)
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Basil Abraham, Danish Goel, Divya Siddarth, Kalika Bali, Manu Chopra, Monojit Choudhury, Pratik Joshi, Preethi Jyoti, Sunayana Sitaram, Vivek Seshadri
| Challenge: | Existing platforms collect labelled speech data from urban speakers whose dialects are often very different from low-income users. |
| Approach: | They propose to collect labelled speech data directly from low-income workers . they collect 109 hours of data from 36 participants in the Marathi language . |
| Outcome: | The proposed approach can provide valuable supplemental earning opportunities to low-income rural and urban workers. |
A Resource for Studying Chatino Verbal Morphology (2020.lrec-1)
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| Challenge: | a recent study focused on the verbal inflectional morphology of a mesoamerican language, San Juan Quiahije Chatino . low-resource endangered languages are left behind, while high-resourced ones are left out . a new approach to document endangered languages is needed to ensure language use continues . |
| Approach: | They present a resource focusing on the verbal inflectional morphology of a mesoamerican language . they use morphological inflection tables and baseline results to assess the language's morphologies . |
| Outcome: | The proposed resource focuses on the verbal inflectional morphology of a mesoamerican language . the authors show that the results are consistent with the current state of the art in the field . |
Learnings from Technological Interventions in a Low Resource Language: A Case-Study on Gondi (2020.lrec-1)
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Devansh Mehta, Sebastin Santy, Ramaravind Kommiya Mothilal, Brij Mohan Lal Srivastava, Alok Sharma, Anurag Shukla, Vishnu Prasad, Venkanna U, Amit Sharma, Kalika Bali
| Challenge: | 40% of all the languages in the world face the danger of extinction in the near future . when a language dies out, future generations lose a vital part of the culture that is necessary to completely understand it. |
| Approach: | They propose to use 4 technology-driven methods of data collection to collect data on Gondi, a low-resource vulnerable language spoken by 2.3 million tribal people in south and central India. |
| Outcome: | The proposed methods collected 12,000 translated words and/or sentences and identified more than 650 community members whose help can be solicited for future translation efforts. |
Irony Detection in Persian Language: A Transfer Learning Approach Using Emoji Prediction (2020.lrec-1)
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Preni Golazizian, Behnam Sabeti, Seyed Arad Ashrafi Asli, Zahra Majdabadi, Omid Momenzadeh, Reza Fahmi
| Challenge: | Existing methods for emotion extraction and sentiment analysis produce invalid results due to the use of irony. |
| Approach: | They propose to use emoji prediction to fine tune a model using hand labeled tweets with irony tags. |
| Outcome: | The proposed method outperforms the state-of-the-art method on Persian dataset with an accuracy of 83.1% and offers strong baseline for further research in Persian language. |
Towards Computational Resource Grammars for Runyankore and Rukiga (2020.lrec-1)
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| Challenge: | In this paper, we present computational resource grammars of Runyankore and Rukiga languages . runyankores and rukiga are under-resourced Bantu languages spoken by 6 million people . |
| Approach: | They present computational resource grammars for Runyankore and Rukiga languages . they use a multilingual grammar formalism and a special- purpose functional programming language . |
| Outcome: | The proposed grammars are the first attempt to create language resources for R&R . they can be used to build computer-aided language learning applications for the languages . |
Optimizing Annotation Effort Using Active Learning Strategies: A Sentiment Analysis Case Study in Persian (2020.lrec-1)
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Seyed Arad Ashrafi Asli, Behnam Sabeti, Zahra Majdabadi, Preni Golazizian, Reza Fahmi, Omid Momenzadeh
| Challenge: | Existing deep learning approaches require huge amounts of data to be trained properly. |
| Approach: | They propose to use Persian as a model to choose the samples for annotation instead of labeling the whole dataset. |
| Outcome: | The proposed models achieve the baseline performance with a significantly lower amount of labeled data. |
BanFakeNews: A Dataset for Detecting Fake News in Bangla (2020.lrec-1)
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| Challenge: | Impact of fake news is creating havoc worldwide. |
| Approach: | They propose an annotated dataset of 50K news that can be used for building automated fake news detection systems for a low resource language like Bangla. |
| Outcome: | The proposed system can be built with state-of-the-art NLP techniques for a low resource language like Bangla. |
A Resource for Computational Experiments on Mapudungun (2020.lrec-1)
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Mingjun Duan, Carlos Fasola, Sai Krishna Rallabandi, Rodolfo Vega, Antonios Anastasopoulos, Lori Levin, Alan W Black
| Challenge: | Low-resource languages still lag behind in documenting endangered languages . a large corpus of culturally significant conversations is available for computational experiments . |
| Approach: | They propose a resource for computational experiments on Mapudungun, a polysynthetic indigenous language spoken in Chile. |
| Outcome: | The proposed corpus provides 142 hours of culturally significant conversations in Mapudungun . the language is spoken by the Mapuche people of southern Chile and western argentina . |
Automated Parsing of Interlinear Glossed Text from Page Images of Grammatical Descriptions (2020.lrec-1)
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| Challenge: | linguistic typology is a subfield of linguistics which studies the design features of human language and the distribution of such features across the languages of the world. |
| Approach: | They propose to parse interlinear glossed text from scanned grammars to make them machinereadable. |
| Outcome: | The proposed technology achieves high precision and recall in the identification of examples sentences in IGT format. |
The Johns Hopkins University Bible Corpus: 1600+ Tongues for Typological Exploration (2020.lrec-1)
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Arya D. McCarthy, Rachel Wicks, Dylan Lewis, Aaron Mueller, Winston Wu, Oliver Adams, Garrett Nicolai, Matt Post, David Yarowsky
| Challenge: | Our corpus spans 1611 diverse written languages, with constituents of more than 90 language families. |
| Approach: | They propose to scrape and merge online resources and merge them with existing corpora to create a verse-parallel scheme for all translations. |
| Outcome: | The results show that the Bible provides high coverage of core vocabulary. |
Towards Building an Automatic Transcription System for Language Documentation: Experiences from Muyu (2020.lrec-1)
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| Challenge: | Language documentation is a rapidly growing field due to its urgency. |
| Approach: | They propose to use phoneme recognition to automatically recognize spoken languages and translate them to global languages. |
| Outcome: | The proposed tool performs better than existing methods with American English, Austrian German and Slovenian as source and target languages. |
Towards Flexible Cross-Resource Exploitation of Heterogeneous Language Documentation Data (2020.lrec-1)
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| Challenge: | a paper on language resource overarching data analysis aims at addressing a complex resource landscape . major challenges arise from the need for cross-resource data analysis and a rather complex resource environment . |
| Approach: | a paper aims to develop methods for language resource overarching data analysis in the field of language documentation. |
| Outcome: | The proposed methods aim to solve the tension between unification of data sets and vocabularies and maximum openness for the integration of future resources and adaption of external information. |
CantoMap: a Hong Kong Cantonese MapTask Corpus (2020.lrec-1)
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| Challenge: | a corpus of connected spoken Hong Kong Cantonese is constructed to study the phonology and semantics of the language. |
| Approach: | They propose to build a corpus of connected spoken Hong Kong Cantonese with phonemic transcription and controlled elicitation tasks. |
| Outcome: | The proposed corpus contains 768 minutes of recordings and transcripts of forty speakers. |
No Data to Crawl? Monolingual Corpus Creation from PDF Files of Truly low-Resource Languages in Peru (2020.lrec-1)
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| Challenge: | Existing methods for extracting text from PDF files are expensive and limited by the absence of web content of endangered languages. |
| Approach: | They propose a method for creating monolingual corpora for four endangered languages . they use a PDF file format with multilingual sentences and noisy pages . |
| Outcome: | The proposed method allows the creation of clean corpora for the four languages, a key resource for natural language processing tasks nowadays. |
Creating a Parallel Icelandic Dependency Treebank from Raw Text to Universal Dependencies (2020.lrec-1)
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| Challenge: | Icelandic language is low-resource and is not yet considered in imminent danger . efforts underway to make it accessible and usable in Language Technology . |
| Approach: | They propose to build a parallel Icelandic dependency treebank based on Universal Dependencies (UD) this is the first parallel treebank resource for the language and several other languages already have one . |
| Outcome: | The proposed treebank is the first parallel treebank resource for the low-resource language . the project will be published as part of UD version 2.6. |
Building a Universal Dependencies Treebank for Occitan (2020.lrec-1)
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Aleksandra Miletic, Myriam Bras, Marianne Vergez-Couret, Louise Esher, Clamença Poujade, Jean Sibille
| Challenge: | Low-resourced regional, non-official or minority languages often face lack of institutional support . low-resource languages often find themselves in a similar situation . |
| Approach: | They propose to create the first treebank for Occitan, a low-resourced regional language . they use an agile annotation approach and rely on pre-processing using existing tools . |
| Outcome: | The proposed treebank is the first for the low-resourced regional language Occitan . the project uses an agile annotation approach and automated pre-annotation . |
Building the Old Javanese Wordnet (2020.lrec-1)
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| Challenge: | Existing wordnets for Old Javanese are limited and lack of an open-source version of the language is a barrier to its development. |
| Approach: | They propose to build a machine readable resource for Old Javanese using the Princeton Wordnet's synsets and semantic hierarchy. |
| Outcome: | The wordnet contains 2,054 concepts or synsets and 5,911 senses. |
CPLM, a Parallel Corpus for Mexican Languages: Development and Interface (2020.lrec-1)
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| Challenge: | Mexico has 68 linguistic groups and 364 varieties, but lack of data on social media and internet is putting them at risk. |
| Approach: | They propose a collaborative corpus for endangered languages in Mexico . they propose linguistic search, digitalization and alignment process for each language . |
| Outcome: | The proposed corpus aligns Spanish with six indigenous languages: Maya, Ch’ol, Mazatec, Mixtec, Otomi, and Nahuatl. |
SiNER: A Large Dataset for Sindhi Named Entity Recognition (2020.lrec-1)
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| Challenge: | Named entity recognition is an essential lower-level task in natural language processing (NLP). |
| Approach: | They propose to develop a named entity recognition dataset for low-resourced Sindhi language with quality baselines. |
| Outcome: | The proposed dataset is likely to be a significant resource for statistical Sindhi language processing. |
Construct a Sense-Frame Aligned Predicate Lexicon for Chinese AMR Corpus (2020.lrec-1)
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| Challenge: | Existing lexicons blur senses and frames of predicates, which needs to be refined to meet word sense disambiguation and event extraction tasks. |
| Approach: | They propose to construct a predicate lexicon for Chinese AMR corpus with 14,389 senses and 10,800 frames of 8,470 words. |
| Outcome: | The proposed lexicon includes 14,389 senses and 10,800 frames of 8,470 words. |
MultiMWE: Building a Multi-lingual Multi-Word Expression (MWE) Parallel Corpora (2020.lrec-1)
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| Challenge: | Existing bilingual or multi-lingual MWE corpora are limited for multilingual use . only 871 pairs of English-German MWEs are available for research . |
| Approach: | They present a collection of bilingual and multi-lingual MWEs extracted from parallel corpora. |
| Outcome: | The available bilingual or multi-lingual MWE corpus is very limited . the collection is a small collection of 871 pairs of English-German MWEs . |
A Myanmar (Burmese)-English Named Entity Transliteration Dictionary (2020.lrec-1)
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| Challenge: | Currently, there are no data available for the transcription of borrowed English words in Myanmar . lack of resources is a problem for many understudied languages . |
| Approach: | They construct a dictionary of Myanmar-English transliteration instances using a CC BY-NC-SA license. |
| Outcome: | The proposed model outperforms the statistical model significantly on the character level. |
CA-EHN: Commonsense Analogy from E-HowNet (2020.lrec-1)
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| Challenge: | Existing word analogy datasets rely on handcrafted words with only dozens of predefined relations. |
| Approach: | They present a commonsense word analogy dataset with 90,505 analogies . they use an ontology that annotates 88K Chinese words with their structured sense definitions and English translations. |
| Outcome: | The proposed dataset shows that word representations embed commonsense knowledge. |
Building Semantic Grams of Human Knowledge (2020.lrec-1)
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| Challenge: | Word senses are typically defined with textual definitions and put in context via lexical-semantic relations such as synonymy, antonymy, hypernymy, etc. |
| Approach: | They propose a slot-filler structure to define the meaning of words in terms of their prototypical semantic information. |
| Outcome: | The proposed model improves on a semantic similarity task and shows significant improvements over state-of-the-art embeddings. |
Automatically Building a Multilingual Lexicon of False Friends With No Supervision (2020.lrec-1)
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| Challenge: | a method to detect false friends from cognates is developed . cognates are words in genetically related languages with a common proto-word . in some cases, cognates have diverged from the common etymon and their meanings became different from each other. |
| Approach: | They propose an automatic method to detect false friends from a set of cognates . cognates are words in genetically related languages which derive from etymons . authors propose a measure of "falseness" of a false friends pair based on cross-lingual word embeddings based in the system . |
| Outcome: | The proposed method can be extended to any language pair, with monolingual corpora and a bilingual dictionary. |
A Parallel WordNet for English, Swedish and Bulgarian (2020.lrec-1)
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| Challenge: | a new WordNet resource for Swedish and Bulgarian is created that is tightly aligned with the Princeton WordNet. |
| Approach: | They propose a WordNet resource for Swedish and Bulgarian that is tightly aligned with Princeton WordNet. |
| Outcome: | The proposed resource is tightly aligned with the Princeton WordNet for Swedish and Bulgarian . the new resource is open-source and in its development used only existing resources. |
ENGLAWI: From Human- to Machine-Readable Wiktionary (2020.lrec-1)
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| Challenge: | ENGLAWI is a structured and normalized version of the English Wiktionary encoded into a workable XML format. |
| Approach: | They introduce ENGLAWI, a large, versatile, XML-encoded machine-readable dictionary extracted from Wiktionary. |
| Outcome: | The proposed lexicographic word embeddings are based on the ENGLAWI definitions and are available for download and are supplied with G-PeTo scripts. |
Opening the Romance Verbal Inflection Dataset 2.0: A CLDF lexicon (2020.lrec-1)
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| Challenge: | lexicon provides verbal paradigm forms in broad IPA phonemic notation for 74 varieties . most resources used to study language evolution computationally rely on multilingual contemporary information . |
| Approach: | They propose a multilingual lexicon of Romance inflection covering 74 varieties . they annotate verbal paradigm forms in broad IPA phonemic notation and organize paradigm cells to reflect cognacy . |
| Outcome: | The lexicon provides verbal paradigm forms in broad IPA phonemic notation for 74 varieties. |
word2word: A Collection of Bilingual Lexicons for 3,564 Language Pairs (2020.lrec-1)
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| Challenge: | Our dataset provides top-k word translations in 3,564 (directed) language pairs across 62 languages in OpenSubtitles2018. |
| Approach: | They propose a dataset and an open-source Python package for cross-lingual word translations extracted from sentence-level parallel corpora. |
| Outcome: | The proposed bilingual lexicons have high coverage and achieve competitive translation quality for several language pairs. |
Introducing Lexical Masks: a New Representation of Lexical Entries for Better Evaluation and Exchange of Lexicons (2020.lrec-1)
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| Challenge: | Existing standards for lexicon format and features are inadequate for evaluation and exchange . lexical masks are a powerful tool used to evaluate and exchange large lexiconic databases . |
| Approach: | They propose a tool to evaluate and exchange lexicon databases in many languages . they propose lexical masks which represent the expected internal structure of a lexico . |
| Outcome: | The proposed lexical masks can be used to evaluate and exchange lexicon databases in many languages. |
A Large-Scale Leveled Readability Lexicon for Standard Arabic (2020.lrec-1)
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| Challenge: | a large-scale leveled readability lexicon for Modern Standard Arabic is not available in many other languages. |
| Approach: | They propose a large-scale leveled readability lexicon for Arabic with 26,000 lemmas . they manually annotate a lexico from three different regions in the arab world . |
| Outcome: | The proposed lexicon is publicly available for Arabic readability tasks. |
Preserving Semantic Information from Old Dictionaries: Linking Senses of the ‘Altfranzösisches Wörterbuch’ to WordNet (2020.lrec-1)
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| Challenge: | Historical dictionaries of the pre-digital period are important resources for the study of older languages. |
| Approach: | They propose to use printed dictionaries to create a more easily accessible and more sustainable lexical database by automating the conversion process. |
| Outcome: | The ‘Altfranzösisches Wörterbuch’, an Old French dictionary published from 1925 onwards, shows how the printed dictionaries can be turned into a more easily accessible and more sustainable lexical database. |
Cifu: a Frequency Lexicon of Hong Kong Cantonese (2020.lrec-1)
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| Challenge: | lexical database for Hong Kong Cantonese offers phonological and orthographic information, frequency measures, and lexically neighborhood information for lexicals in HKC. |
| Approach: | They introduce a lexical database for Hong Kong Cantonese that offers phonological and orthographic information, frequency measures, and lexically neighborhood information for lexicals in HKC. |
| Outcome: | The proposed lexical database for Hong Kong Cantonese offers phonological and orthographic information, frequency measures, and lexically neighborhood information. |
Odi et Amo. Creating, Evaluating and Extending Sentiment Lexicons for Latin. (2020.lrec-1)
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| Challenge: | a new paper aims to provide sentiment analysis tools for ancient languages . the current sentiment analysis resources only cover modern languages based on textual typologies . |
| Approach: | They propose to use manually-curated Latin lexicons to evaluate sentiment analysis tools . they propose a gold standard and a silver standard for evaluating lexical items . |
| Outcome: | The proposed lexicons are evaluated using a gold standard and a silver standard for sentiment analysis. |
WordWars: A Dataset to Examine the Natural Selection of Words (2020.lrec-1)
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| Challenge: | a growing body of work on how word meaning changes over time is mutation . a new dataset, WordWars, explores how word success changes over the time . |
| Approach: | They analyze a dataset of 5000 English words in synsets and examine natural selection . they find frequency, length, and concreteness all impact natural selection, they say . |
| Outcome: | a new dataset shows that one third of the synsets undergo a change in the predominant word in this time period. |
Challenge Dataset of Cognates and False Friend Pairs from Indian Languages (2020.lrec-1)
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| Challenge: | Cognates are words that have a common etymological origin and can facilitate the Second Language Acquisition (SLA) however, they also pose a challenge to various NLP applications such as Machine Translation and Cross-lingual Sense Disambiguation. |
| Approach: | They create two cognate datasets for twelve Indian languages and use them to generate cognate sets. |
| Outcome: | The proposed datasets are curated using previously available baseline cognate detection approaches and evaluated with the help of lexicographers. |
Development of a Japanese Personality Dictionary based on Psychological Methods (2020.lrec-1)
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| Challenge: | a new approach to constructing a personality dictionary with psychological evidence is needed . we use abstract terms such as "sociable person" or "kind" to describe ourselves or others . |
| Approach: | They propose a Japanese personality dictionary with weights for Big Five traits . they collect personality words and use word embeddings to construct the dictionary . |
| Outcome: | The proposed approach is the first to have psychological evidence tolerant to NLP standards. |
A Lexicon-Based Approach for Detecting Hedges in Informal Text (2020.lrec-1)
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| Challenge: | Existing studies on hedging detection have focused on structured texts and formal communications. |
| Approach: | They propose to use hedging words and phrases to identify tensions between interviewees during a survivor interview to help researchers understand the dynamics of the interview. |
| Outcome: | The proposed algorithm detects sentence-level hedges in informal conversations such as survivor interviews. |
Word Complexity Estimation for Japanese Lexical Simplification (2020.lrec-1)
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| Challenge: | Experimental results show that the proposed method achieves the highest performance of Japanese lexical simplification. |
| Approach: | They propose a large-scale word complexity lexicon, a synonym lexicone and a toolkit for developing and benchmarking Japanese lexical simplification systems. |
| Outcome: | The proposed method achieves the highest performance of Japanese lexical simplification. |
Inducing Universal Semantic Tag Vectors (2020.lrec-1)
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| Challenge: | Existing semantic tags are useful for syntactically oriented downstream NLP tasks . but their size is limited and many words are out-of-vocabulary words . |
| Approach: | They propose to tagging words with semantic distinctions that are likely to be useful across semantic tasks. |
| Outcome: | The proposed semantic tagging scheme can predict unseen words with high accuracy . it distinguishes privative attributes from subsective ones, making it easier to discern fake detectives . |
LexiDB: Patterns & Methods for Corpus Linguistic Database Management (2020.lrec-1)
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| Challenge: | LexiDB is a tool for storing, managing and querying corpus data. |
| Approach: | They propose to use LexiDB for storing, managing and querying corpus data. |
| Outcome: | The proposed methods outperform existing tools for corpus queries and storage. |
Towards a Semi-Automatic Detection of Reflexive and Reciprocal Constructions and Their Representation in a Valency Lexicon (2020.lrec-1)
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| Challenge: | valency lexicons describe valencies of verbs in non-reflexive and non-reciprocal constructions . reflexive and reciprocal constructions are common morphosyntactic forms of verb . |
| Approach: | They propose a semi-automatic procedure to detect verbs with reflexive and reciprocal constructions in corpus data. |
| Outcome: | The proposed procedure detects verbs that form reflexive and reciprocal constructions in corpus data. |
Languages Resources for Poorly Endowed Languages : The Case Study of Classical Armenian (2020.lrec-1)
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| Challenge: | Classical Armenian is a poorly endowed language with limited resources . the lack of precise and complete grammatical and lexicographical resources remains . |
| Approach: | This article presents the new resources provided on the Calfa platform for Classical Armenian. |
| Outcome: | The Calfa project provides the richest database for Classical Armenian today. |
Constructing Web-Accessible Semantic Role Labels and Frames for Japanese as Additions to the NPCMJ Parsed Corpus (2020.lrec-1)
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| Challenge: | Adding semantic role labels to the NPCMJ will help language learners and linguists search for syntactic and semantic features. |
| Approach: | They propose to add frame information for predicates and two types of semantic role labels that mark contributions of arguments to the NINJAL Parsed Corpus of Modern Japanese (NPCMJ) this will provide a web-accessible language resource for linguists and language learners searching for syntactic and semantic features. |
| Outcome: | The proposed framework will be able to search examples of Japanese for syntactic and semantic features. |
Large-scale Cross-lingual Language Resources for Referencing and Framing (2020.lrec-1)
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| Challenge: | Existing corpora that capture language but do not represent actual situations hinder development of systems to resolve cross-document coreference. |
| Approach: | They introduce the concept of cross-lingual referential corpora and propose a framework to analyze framing . they expect to capture larger variation in framation compared to traditional approaches . |
| Outcome: | The proposed project will analyze the framing of incidents in different languages and texts . it expects to capture larger variation in framation compared to traditional approaches . |
Modelling Etymology in LMF/TEI: The Grande Dicionário Houaiss da Língua Portuguesa Dictionary as a Use Case (2020.lrec-1)
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| Challenge: | In this article, we will introduce two of the new parts of the Lexical Markup Framework (LMF) ISO standard . part 3 deals with etymological and diachronic data and part 4 consists of a TEI serialisation of all of the prior parts of TEIS model. |
| Approach: | They introduce two parts of the Lexical Markup Framework (LMF) ISO standard, part 3 dealing with etymological and diachronic data and part 4 containing TEI serialisation of all prior parts of a model. |
| Outcome: | The proposed models are based on examples taken from a Portuguese dictionary conversion and are then compared with TEI-XML models. |
Linking the TUFS Basic Vocabulary to the Open Multilingual Wordnet (2020.lrec-1)
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| Challenge: | The TUFS Basic Vocabulary Modules are hand created, using commonly occurring vocabulary. |
| Approach: | They propose to link the TUFS Basic Vocabulary Modules with the Open Multilingual Wordnet to create a multilingual lexicon. |
| Outcome: | The proposed lexicons can be used to evaluate existing wordnets, add data to wordnet synsets and create new open wordnet for Khmer, Korean, Lao, Mongolian, Russian, Tagalog, Urdua nd Vietnamese. |
Some Issues with Building a Multilingual Wordnet (2020.lrec-1)
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| Challenge: | Notable extensions include: confidence, corpus frequency, orthographic variants, lexicalized and non-lexicalised synsets and lemmas, new parts of speech, and more. |
| Approach: | They propose to integrate a new open multilingual wordnet format that tests the extensions introduced by the new format and integrates a set of tools to ensure the integrity of the Collaborative Interlingual Index. |
| Outcome: | The proposed format integrates a set of tools that test the extensions while ensuring the integrity of the Collaborative Interlingual Index (CILI). |
Collocations in Russian Lexicography and Russian Collocations Database (2020.lrec-1)
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| Challenge: | Existing methods for collocation extraction cannot be considered perfect, argues a new study. |
| Approach: | They propose to build a database that will include dictionary and statistical collocations in Russian . the database will be based on dictionaries and online systems that describe collocation . |
| Outcome: | The proposed database will include dictionary and statistical collocations in Russian . the results can be useful for machine learning and for other NLP tasks . |
Methodological Aspects of Developing and Managing an Etymological Lexical Resource: Introducing EtymDB-2.0 (2020.lrec-1)
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| Challenge: | Diachronic lexical information is increasingly used in historical linguistics and in NLP . etymological resources need to be fine-grained, large-coverage and accurate . |
| Approach: | They propose guidelines to generate etymological lexical resources for each step of the life-cycle of an ethymology . they introduce EtymDB 2.0, an 'etiological database' generated from the Wiktionary . |
| Outcome: | The proposed resources are generated for each step of the life-cycle of an etymological lexicon: creation, update, evaluation, dissemination, and exploitation. |
OFrLex: A Computational Morphological and Syntactic Lexicon for Old French (2020.lrec-1)
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| Challenge: | Using heterogeneous language resources, we extract structured and exploitable information from a large-coverage morphological and syntactic Old French lexicon. |
| Approach: | They propose to use a large-coverage morphological and syntactic Old French lexicon to extract structured and exploitable information from heterogeneous language resources. |
| Outcome: | The proposed extension technique will be validated manually in the near future and take advantage of OFrLex’s viewing, searching and editing interface. |
Automatic Reconstruction of Missing Romanian Cognates and Unattested Latin Words (2020.lrec-1)
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| Challenge: | Existing methods for producing related words are based on sequence labeling . |
| Approach: | They propose a method for producing related words based on sequence labeling . they aim to fill in gaps in incomplete cognate sets in Romance languages with Latin etymology and reconstruct uncertified Latin words. |
| Outcome: | The proposed method fills in gaps in incomplete cognate sets in Romance languages with Latin etymology and reconstructs uncertified Latin words. |
A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment (2020.lrec-1)
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Sina Ahmadi, John Philip McCrae, Sanni Nimb, Fahad Khan, Monica Monachini, Bolette Pedersen, Thierry Declerck, Tanja Wissik, Andrea Bellandi, Irene Pisani, Thomas Troelsgård, Sussi Olsen, Simon Krek, Veronika Lipp, Tamás Váradi, László Simon, András Gyorffy, Carole Tiberius, Tanneke Schoonheim, Yifat Ben Moshe, Maya Rudich, Raya Abu Ahmad, Dorielle Lonke, Kira Kovalenko, Margit Langemets, Jelena Kallas, Oksana Dereza, Theodorus Fransen, David Cillessen, David Lindemann, Mikel Alonso, Ana Salgado, José Luis Sancho, Rafael-J. Ureña-Ruiz, Jordi Porta Zamorano, Kiril Simov, Petya Osenova, Zara Kancheva, Ivaylo Radev, Ranka Stanković, Andrej Perdih, Dejan Gabrovsek
| Challenge: | a new dataset aims to align monolingual dictionaries with a single sense level for 15 languages . this dataset covers a wide range of languages and resources . |
| Approach: | They propose to manually align monolingual dictionaries with possible semantic relationships . they use 15 languages to create a new baseline for the task of monolingual word sense alignment . |
| Outcome: | The proposed dataset covers 15 languages and covers the more challenging task of linking general-purpose language. |
A Broad-Coverage Deep Semantic Lexicon for Verbs (2020.lrec-1)
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| Challenge: | a lack of a broad-coverage deep semantic lexicon hinders deep language understanding . we have developed a resource for verbs with the coverage of WordNet and syntactic and semantic details . |
| Approach: | They propose a deep lexical resource for verbs with the coverage of WordNet and syntactic and semantic details that meet or exceed existing resources. |
| Outcome: | The proposed resource has the coverage of WordNet and syntactic and semantic details that exceed existing resources. |
Computational Etymology and Word Emergence (2020.lrec-1)
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| Challenge: | etymology is the study of words' origins. |
| Approach: | They develop an extensible Wiktionary parser that predicts the etymology of a word across the full range of ethymological types and languages in Wiktionaries. |
| Outcome: | The proposed parser predicts the etymology of a word across the full range of ethymologies and languages in Wiktionary, and shows the application of tymatics in modeling this phenomenon. |
A Dataset of Translational Equivalents Built on the Basis of plWordNet-Princeton WordNet Synset Mapping (2020.lrec-1)
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| Challenge: | a dataset of 11,000 Polish-English translational equivalents is presented . the dataset is a novum in the wordnet domain and can facilitate the precision of bilingual NLP tasks. |
| Approach: | They present a dataset of Polish-English translational equivalents linked by three types of equivalence links. |
| Outcome: | The proposed dataset contains 11,000 Polish-English translational equivalents . the resulting subsets are based on a manual annotation process and a set of formal features . |
TRANSLIT: A Large-scale Name Transliteration Resource (2020.lrec-1)
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| Challenge: | Transliteration is the process of expressing a proper name from a source language in the characters of a target language. |
| Approach: | They present a large-scale corpus of transliterated names in 180 languages . they use machine learning to train automatic transliteration . |
| Outcome: | The proposed system achieves 92% accuracy on identification of transliterated pairs. |
Computing with Subjectivity Lexicons (2020.lrec-1)
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Caio L. M. Jeronimo, Claudio E. C. Campelo, Leandro Balby Marinho, Allan Sales, Adriano Veloso, Roberta Viola
| Challenge: | a new set of lexicons for expressing subjectivity in text documents is presented . lexiconics are useful resources for identifying semantics relevant to sentiment, emotion, personality, language bias, mood, and attitude. |
| Approach: | They propose a set of lexicons for expressing subjectivity in Brazilian Portuguese text documents . they use word embedding techniques to capture semantically related words to the ones in the lexicos . |
| Outcome: | The proposed lexicons represent different subjectivity dimensions and are more compact in number of terms. |
The ACoLi Dictionary Graph (2020.lrec-1)
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| Challenge: | ACoLi Dictionary Graph is a collection of multilingual open source dictionaries available in two machine-readable formats. |
| Approach: | They propose to map and harmonize ACoLi Dictionary Graph into a unified representation and a tabular data format to facilitate their use in NLP tasks. |
| Outcome: | The ACoLi Dictionary Graph is a collection of multilingual open source dictionaries available in two machine-readable formats. |
Resources in Underrepresented Languages: Building a Representative Romanian Corpus (2020.lrec-1)
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Ludmila Midrigan - Ciochina, Victoria Boyd, Lucila Sanchez-Ortega, Diana Malancea_Malac, Doina Midrigan, David P. Corina
| Challenge: | Currently, the corpus has approximately 5,500,000 tokens originating from written text and 100,000 tokens of spoken language. |
| Approach: | They describe the process of creating a large and representative corpus in Romanian, a relatively under-resourced language with unique typological characteristics. |
| Outcome: | The proposed corpus contains 5,500,000 tokens originating from written text and 100,000 tokens of spoken language. |
World Class Language Technology - Developing a Language Technology Strategy for Danish (2020.lrec-1)
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| Challenge: | Danish government adopts ambitious strategy for LT and artificial intelligence . 35 million DKK will be spent over a period of 6 years to develop platform . |
| Approach: | They describe the process behind the development of the language-related parts of the strategy . they describe how focus areas and recommendations for the LT strategy were established . |
| Outcome: | The Danish government adopted a new, ambitious strategy for LT and AI in March 2019 . the focus areas and recommendations for the LT strategy were established based on user feedback . |
A Corpus for Automatic Readability Assessment and Text Simplification of German (2020.lrec-1)
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| Challenge: | Using monolingual-only data, we can automate readability assessment and text simplification of simplified language. |
| Approach: | They present a corpus for automatic readability assessment and automatic text simplification for German using parallel and monolingual data. |
| Outcome: | The proposed corpus is compiled from web sources and contains information on text structure, typography, font style, and images. |
The CLARIN Knowledge Centre for Atypical Communication Expertise (2020.lrec-1)
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| Challenge: | ACE is a new knowledge center for Atypical communication experts . it is located at the Centre for Language and Speech Technology (CLST) at Radboud University . |
| Approach: | They introduce a new CLARIN Knowledge Center called the K-Centre for Atypical Communication Expertise (ACE) ACE closely collaborates with The Language Archive at the Max Planck Institute for Psycholinguistics to safeguard GDPR-compliant data storage and access. |
| Outcome: | The new CLARIN Knowledge Center is the K-Centre for Atypical Communication Expertise (ACE) ACE closely collaborates with The Language Archive (TLA) at the Max Planck Institute for Psycholinguistics in order to safeguard GDPR-compliant data storage and access. |
Corpora of Disordered Speech in the Light of the GDPR: Two Use Cases from the DELAD Initiative (2020.lrec-1)
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| Challenge: | Corpora of disordered speech (CDS) are costly to collect and difficult to share due to personal data protection and IP issues. |
| Approach: | a new paper examines the legal grounds for processing corpora of disordered speech . it illustrates how consent and public interest are taken into consideration . the paper also examines how public interest research can be used to obtain consent . |
| Outcome: | a new study examines the legal grounds for processing corpora of disordered speech (CDS) two use cases illustrate the legal basis for processing CDS in light of the GDPR . |
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe (2020.lrec-1)
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Georg Rehm, Katrin Marheinecke, Stefanie Hegele, Stelios Piperidis, Kalina Bontcheva, Jan Hajič, Khalid Choukri, Andrejs Vasiļjevs, Gerhard Backfried, Christoph Prinz, José Manuel Gómez-Pérez, Luc Meertens, Paul Lukowicz, Josef van Genabith, Andrea Lösch, Philipp Slusallek, Morten Irgens, Patrick Gatellier, Joachim Köhler, Laure Le Bars, Dimitra Anastasiou, Albina Auksoriūtė, Núria Bel, António Branco, Gerhard Budin, Walter Daelemans, Koenraad De Smedt, Radovan Garabík, Maria Gavriilidou, Dagmar Gromann, Svetla Koeva, Simon Krek, Cvetana Krstev, Krister Lindén, Bernardo Magnini, Jan Odijk, Maciej Ogrodniczuk, Eiríkur Rögnvaldsson, Mike Rosner, Bolette Pedersen, Inguna Skadiņa, Marko Tadić, Dan Tufiș, Tamás Váradi, Kadri Vider, Andy Way, François Yvon
| Challenge: | Language Technologies (LTs) are a powerful means to break down language barriers impacting business, cross-lingual and cross-cultural communication in Europe. |
| Approach: | They present an overview of the European LT landscape and the current state of play in industry and the LT market. |
| Outcome: | The present study outlines funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. |
A Framework for Shared Agreement of Language Tags beyond ISO 639 (2020.lrec-1)
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| Challenge: | Identification and annotation of languages in an unambiguous and standardized way is essential for the description of linguistic data. |
| Approach: | They propose a pattern that extends the BCP 47 sub-tag ‘privateuse’ and is able to overcome the limits of BCP47 and ISO 639. |
| Outcome: | The proposed pattern overcomes the limitations of BCP 47 and ISO 639 for the identification of lesser-known languages, endangered languages, regional varieties or historical stages of a language. |
Gigafida 2.0: The Reference Corpus of Written Standard Slovene (2020.lrec-1)
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Simon Krek, Špela Arhar Holdt, Tomaž Erjavec, Jaka Čibej, Andraz Repar, Polona Gantar, Nikola Ljubešić, Iztok Kosem, Kaja Dobrovoljc
| Challenge: | Gigafida reference corpus of Slovene is updated with new material and tools . focus of upgrade was on transformation from general reference corp to standard reference corp . |
| Approach: | We present a new version of the Gigafida reference corpus of Slovene . the upgrade includes new material and better tools for annotating it . |
| Outcome: | The new version of the Gigafida reference corpus of Slovene is described . the whole Gigido corpus was deduplicated for the first time . |
Corpus Query Lingua Franca part II: Ontology (2020.lrec-1)
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| Challenge: | outlines the projected second part of the Corpus Query Lingua Franca (CQLF) family of standards . the existence of a large number of different corpus query languages poses an epistemic challenge for the research community . |
| Approach: | They propose to standardize the Corpus Query Lingua Franca (CQLF) family of standards . they present the assumptions and aims of the CQLF Metamodel and its basic structure . |
| Outcome: | The proposed second part of the Corpus Query Lingua Franca (CQLF) family is in the process of standardization at the International Standards Organization (ISO) the first part of CQLF Ontology was adopted as an international standard at the beginning of 2018 . |
A CLARIN Transcription Portal for Interview Data (2020.lrec-1)
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| Challenge: | a transcription portal for audio files based on automatic speech recognition (ASR) is implemented in the CLARIN resources research network and intended for use by non-technical scholars. |
| Approach: | They propose a transcription portal for audio files based on automatic speech recognition in various languages. |
| Outcome: | The proposed transcription portal is implemented in the CLARIN resources research network and intended for use by non-technical scholars. |
Ellogon Casual Annotation Infrastructure (2020.lrec-1)
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| Challenge: | Existing annotation tools are desktop applications, allowing the annotation of corpora found on a single computer. |
| Approach: | They propose a new annotation paradigm, casual annotation, and propose an architecture and a reference implementation for the Ellogon Casual Annotation Tool. |
| Outcome: | The proposed paradigm and architecture have been evaluated for more than two years on an annotation task related to sentiment analysis. |
European Language Grid: An Overview (2020.lrec-1)
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Georg Rehm, Maria Berger, Ela Elsholz, Stefanie Hegele, Florian Kintzel, Katrin Marheinecke, Stelios Piperidis, Miltos Deligiannis, Dimitris Galanis, Katerina Gkirtzou, Penny Labropoulou, Kalina Bontcheva, David Jones, Ian Roberts, Jan Hajič, Jana Hamrlová, Lukáš Kačena, Khalid Choukri, Victoria Arranz, Andrejs Vasiļjevs, Orians Anvari, Andis Lagzdiņš, Jūlija Meļņika, Gerhard Backfried, Erinç Dikici, Miroslav Janosik, Katja Prinz, Christoph Prinz, Severin Stampler, Dorothea Thomas-Aniola, José Manuel Gómez-Pérez, Andres Garcia Silva, Christian Berrío, Ulrich Germann, Steve Renals, Ondrej Klejch
| Challenge: | European LT business is dominated by hundreds of SMEs and a few large players, with technologies that outperform the global players. |
| Approach: | European Language Grid (ELG) project addresses this by establishing the ELG as the primary platform for LT in Europe. |
| Outcome: | European Language Grid (ELG) will be primary platform for LT in Europe . it will provide access to hundreds of commercial and non-commercial LTs for all European languages, including running tools and services as well as data sets and resources. |
The Competitiveness Analysis of the European Language Technology Market (2020.lrec-1)
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Andrejs Vasiļjevs, Inguna Skadiņa, Indra Samite, Kaspars Kauliņš, Ēriks Ajausks, Jūlija Meļņika, Aivars Bērziņš
| Challenge: | The study focuses on three LT areas of the greatest interest for the ECmachine translation (MT), speech technology, and cross-lingual search. |
| Approach: | This paper presents the key results of a competitiveness analysis of the European language technology market for three areas – Machine Translation, speech technology, and cross-lingual search. |
| Outcome: | The study focuses on three LT areas of the greatest interest for the ECmachine translation (MT), speech technology, and cross-lingual search. |
Constructing a Bilingual Hadith Corpus Using a Segmentation Tool (2020.lrec-1)
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| Challenge: | Existing studies on Hadith have focused on the Quran, leaving it relatively unexplored. |
| Approach: | They propose to gather and construct a bilingual parallel corpus of Islamic Hadith using a custom segmentation tool that annotates the two Hadithe components with 92% accuracy. |
| Outcome: | The proposed method minimises the costs of language resource creation and produces consistent results independently from previous knowledge and experiences that usually influence human annotators. |
Facilitating Corpus Usage: Making Icelandic Corpora More Accessible for Researchers and Language Users (2020.lrec-1)
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| Challenge: | Gigaword corpus is a large text corpus used in natural language processing . large corpora are needed to achieve better performance in the field of NLP . |
| Approach: | They propose a set of tools to facilitate the use of the Icelandic Gigaword Corpus . they provide n-grams based on the corpus, and a variety of pre-trained word embeddings models . |
| Outcome: | The proposed tools facilitate the use of the Icelandic Gigaword corpus in the field of Natural Language Processing and other fields. |
Interoperability in an Infrastructure Enabling Multidisciplinary Research: The case of CLARIN (2020.lrec-1)
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| Challenge: | CLARIN supports the use and study of language data in general and aims to increase the potential for comparative research of cultural and societal phenomena across languages and disciplines. |
| Approach: | They describe the interoperability requirements that arise through the existing ambitions and emerging frameworks. |
| Outcome: | The proposed frameworks will address interoperability requirements at several levels, including organisation and ecosystem, design of workflow services, data curation, performance measurement and collaboration. |
Language Technology Programme for Icelandic 2019-2023 (2020.lrec-1)
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Anna Nikulásdóttir, Jón Guðnason, Anton Karl Ingason, Hrafn Loftsson, Eiríkur Rögnvaldsson, Einar Freyr Sigurðsson, Steinþór Steingrímsson
| Challenge: | a new national language technology programme for Icelandic is described . the programme aims to make Icelandic usable in communication and interactions in the digital world . |
| Approach: | They describe a new national language technology programme for Icelandic . the programme aims to make Icelandic usable in communication and interactions in the digital world . |
| Outcome: | The proposed programme aims to make Icelandic usable in communication and interactions in the digital world. |
Privacy by Design and Language Resources (2020.lrec-1)
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| Challenge: | Privacy by Design is an approach in which privacy and data protection are embedded throughout the project lifecycle . the principle of Privacy by design was first mentioned in the 1995 EU Data Protection Directive . |
| Approach: | a paper proposes to analyze the practical meaning of Privacy by Design in the context of Language Resources . the paper propose measures and safeguards that can be implemented by the community to ensure respect of this principle. |
| Outcome: | The proposed paper analyzes the practical meaning of Privacy by Design in the context of Language Resources . proposed safeguards can be implemented by the community to ensure respect of this principle. |
Making Metadata Fit for Next Generation Language Technology Platforms: The Metadata Schema of the European Language Grid (2020.lrec-1)
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Penny Labropoulou, Katerina Gkirtzou, Maria Gavriilidou, Miltos Deligiannis, Dimitris Galanis, Stelios Piperidis, Georg Rehm, Maria Berger, Valérie Mapelli, Michael Rigault, Victoria Arranz, Khalid Choukri, Gerhard Backfried, José Manuel Gómez-Pérez, Andres Garcia-Silva
| Challenge: | Metadata are a key factor in the management, sharing and usage of digital assets . the European Language Grid project aims to be the primary hub and marketplace for industry-relevant Language Technology in Europe. |
| Approach: | They propose a rich metadata schema catering for the description of Language Resources and Technologies. |
| Outcome: | The proposed schema powers the European Language Grid platform that aims to be the primary hub and marketplace for industry-relevant Language Technology in Europe. |
Related Works in the Linguistic Data Consortium Catalog (2020.lrec-1)
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| Challenge: | Existing metadata standards for Related Works are used to define relations between language resources. |
| Approach: | They describe the development and implementation of a Related Works schema and the steps to implementation. |
| Outcome: | The proposed schema has been implemented in the Linguistic Data Consortium's (LDC) Catalog. |
Language Data Sharing in European Public Services – Overcoming Obstacles and Creating Sustainable Data Sharing Infrastructures (2020.lrec-1)
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| Challenge: | Data is key in training modern language technologies. |
| Approach: | They summarise findings of first pan-European study on barriers to language data sharing . they identify structural challenges, disposition towards CAT tools and lack of digital skills . overcoming language barriers is one of the main challenges european citizens face . |
| Outcome: | The paper summarises the findings of the first pan-European study on barriers to language data sharing . the findings highlight the barriers and recommend solutions to overcome them . |
A Progress Report on Activities at the Linguistic Data Consortium Benefitting the LREC Community (2020.lrec-1)
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Christopher Cieri, James Fiumara, Stephanie Strassel, Jonathan Wright, Denise DiPersio, Mark Liberman
| Challenge: | Linguistic Data Consortium (LDC) activities include the collection, annotation, processing, distribution, archiving and curation of language resources. |
| Approach: | a new report sketches the activities of a data center devoted to supporting the work of LREC attendees . 96 new corpora released in 2018-2020 to date, a technology evaluation campaign and innovations to advance methodology for language data collection and annotation. |
| Outcome: | 96 new corpora released in 2018-2020 to date, new technology evaluation campaign and innovations to advance methodology of language data collection and annotation. |
Digital Language Infrastructures – Documenting Language Actors (2020.lrec-1)
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| Challenge: | Existing language infrastructures focus on large institutions, but smaller institutions could benefit from them. |
| Approach: | They propose to reach out to smaller local language actors on a local scope . they highlight the need to connect these institutions to existing infrastructures . |
| Outcome: | The proposed project aims to reach out to smaller local language actors on a local scope and discuss challenges related to this ambition. |
Samrómur: Crowd-sourcing Data Collection for Icelandic Speech Recognition (2020.lrec-1)
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David Erik Mollberg, Ólafur Helgi Jónsson, Sunneva Þorsteinsdóttir, Steinþór Steingrímsson, Eydís Huld Magnúsdóttir, Jon Gudnason
| Challenge: | Samrómur is a web application built upon Mozilla Foundation’s open-source voice collection platform Common Voice. |
| Approach: | They describe an ongoing project of speech data collection using the web application Samrómur which is built upon Common Voice, Mozilla Foundation’s web platform for open-source voice collection. |
| Outcome: | The proposed system will be the largest open speech corpus for Icelandic collected from the public domain. |
Semi-supervised Development of ASR Systems for Multilingual Code-switched Speech in Under-resourced Languages (2020.lrec-1)
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| Challenge: | Existing models for code-switching between languages are under-resourced and limited by text and acoustic data. |
| Approach: | They propose to construct four separate bilingual automatic speech recognisers corresponding to four different language pairs between which speakers switch frequently. |
| Outcome: | The proposed models are compared with a non-batch-wise approach and show that they perform better when used with sparse training data. |
CLFD: A Novel Vectorization Technique and Its Application in Fake News Detection (2020.lrec-1)
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| Challenge: | Existing work on fake news detection is limited due to the complex nature of the news . |
| Approach: | They propose a statistical approach for the generation of feature vectors to describe a document . they use class label frequency distance to boost machine learning methods . |
| Outcome: | The proposed method outperforms deep learning methods in large datasets while outperforming traditional methods. |
SimplifyUR: Unsupervised Lexical Text Simplification for Urdu (2020.lrec-1)
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| Challenge: | Existing methods for text simplification for Urdu rely on manual lexicons and simplified corpora, but are not applicable to the language. |
| Approach: | They propose an unsupervised method for automatic text simplification for Urdu using word embeddings and morphological features. |
| Outcome: | The proposed method achieves BLEU score of 80.15 and SARI score of 42.02 on simple text generated on simplified corpora and human evaluations for correctness, grammaticality, meaning-preservation and simplicity. |
Jamo Pair Encoding: Subcharacter Representation-based Extreme Korean Vocabulary Compression for Efficient Subword Tokenization (2020.lrec-1)
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| Challenge: | a vocabulary size problem is unsolved in multilingual pre-training tasks. |
| Approach: | They propose two algorithms that increase the elasticity of budget for building the vocabulary in Byte-Pair Encoding inspired tokenizers, and reduce the cost of supporting Korean in a multilingual model. |
| Outcome: | The proposed method reduces the cost of supporting Korean in a multilingual model. |
Offensive Language and Hate Speech Detection for Danish (2020.lrec-1)
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| Challenge: | a growing number of social media platforms are detecting and dealing with offensive language . a recent study found that the best performing system for English is best for Danish . |
| Approach: | They propose automatic methods to detect offensive language on social media platforms . they use user-generated comments from various social media sites to find offensive language . |
| Outcome: | The proposed system performs best for both English and Danish language . it achieves a macro averaged F1-score of 0.74 and a best for Danish achieves 0.73 . |
Semi-supervised Deep Embedded Clustering with Anomaly Detection for Semantic Frame Induction (2020.lrec-1)
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| Challenge: | Empirical results show that definitions provide contextual information for representing and characterizing the frame membership of lexical units. |
| Approach: | They propose a two-step frame induction process to remove lexical units that cannot fit into existing frames in Berkeley FrameNet. |
| Outcome: | The proposed method outperforms state-of-the-art methods in both steps of the frame induction process. |
Search Query Language Identification Using Weak Labeling (2020.lrec-1)
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| Challenge: | a recent study has shown that language identification is a well-known task for natural language documents. |
| Approach: | They propose a search query language identification task that trains large-scale query-language pairs for training without loss of generalization. |
| Outcome: | The proposed model outperforms open domain model baselines by a large margin. |
Automated Phonological Transcription of Akkadian Cuneiform Text (2020.lrec-1)
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| Challenge: | Akkadian was an east-semitic language spoken in ancient Mesopotamia . cuneiform text does not mark the inflection for logograms, so the inflected form needs to be inferred from the sentence context. |
| Approach: | They propose to automate phonological transcription of the transliterated Akkadian corpora . transcription is normalized according to the grammatical description of a given dialect . they find that cuneiform text does not mark the inflection for logograms . |
| Outcome: | The proposed transcriptions show the Akkadian renderings for Sumerian logograms, while the logogram transcription is more challenging. |
COSTRA 1.0: A Dataset of Complex Sentence Transformations (2020.lrec-1)
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| Challenge: | Several studies have shown word vector representations capture syntactic and semantic regularities. |
| Approach: | They present a dataset of sentence-level embeddings in Czech and plan to use it in other languages. |
| Outcome: | The proposed dataset is limited to Czech but will be used in other languages. |
Automatic In-the-wild Dataset Annotation with Deep Generalized Multiple Instance Learning (2020.lrec-1)
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| Challenge: | Existing methods to label large datasets that resemble real life situations are prohibitive due to the cost of manual labeling. |
| Approach: | They propose to automate the annotation process by using end-to-end differentiable neural networks to label large datasets that resemble real life conditions. |
| Outcome: | The proposed method can label a large dataset in the wild without human intervention without any cost. |
How Much Data Do You Need? About the Creation of a Ground Truth for Black Letter and the Effectiveness of Neural OCR (2020.lrec-1)
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| Challenge: | Recent advances in Optical Character Recognition and Handwritten Text Recognition have led to more accurate text recognition of historical documents. |
| Approach: | They propose to build a ground truth for a German-language newspaper published in black letter . they also evaluate the performance of different OCR engines and estimate how much data is needed to achieve high-quality OCR results. |
| Outcome: | The proposed model can recognise black letter text and performs well on data they have not seen during training. |
Dirichlet-Smoothed Word Embeddings for Low-Resource Settings (2020.lrec-1)
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| Challenge: | Existing count-based word embeddings are superseded by machine-learning methods like word2vec and GloVe, but in many settings there is not much text data available. |
| Approach: | They propose to use positive pointwise mutual information (PPMI) weighted co-occurrence matrices to compute word embeddings from a corpus using large amounts of text data. |
| Outcome: | The proposed method outperforms word2vec and the state-of-the-art for low-resource settings and obtains competitive results for Maltese and Luxembourgish. |
On The Performance of Time-Pooling Strategies for End-to-End Spoken Language Identification (2020.lrec-1)
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| Challenge: | Language identification (LID) from speech is commonly tackled using similar approaches to those employed for speaker verification/recognition. |
| Approach: | They propose to combine local descriptors and global descriptores into a single global description that can be used for downstream classification tasks. |
| Outcome: | The proposed methods outperform well-known benchmark systems and previously results based on attention only. |
Neural Disambiguation of Lemma and Part of Speech in Morphologically Rich Languages (2020.lrec-1)
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| Challenge: | a method for disambiguating the lemma and part of speech of ambiguous words is proposed . a morphological analyser produces multiple analyses for ambiguously words . |
| Approach: | They propose a method for disambiguating the lemma and part of speech of ambiguous words in context . they use a large un-annotated corpus of text and a morphological analyser to train neural networks on the output of the analyser . |
| Outcome: | The proposed method outperforms the state-of-the-art on POS and lemma disambiguation in morphologically rich languages using no manual disambiguations or data annotations. |
Non-Linearity in Mapping Based Cross-Lingual Word Embeddings (2020.lrec-1)
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| Challenge: | Existing work on cross-lingual word embeddings rely on linear mappings, but this assumption is not true for all language pairs. |
| Approach: | They propose a non-linear mapping approach which can find non-linesar relationships between languages by kernel Canonical Correlation Analysis. |
| Outcome: | The proposed approach improves on five language pairs on supervised and self-learning scenarios. |
LibriVoxDeEn: A Corpus for German-to-English Speech Translation and German Speech Recognition (2020.lrec-1)
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| Challenge: | a corpus of sentence-aligned triples of German audio, German text, and English translation is available for speech recognition . a large corpus is available to date for end-to-end speech translation based on parallel data . |
| Approach: | They present a corpus of sentence-aligned triples of German audio, German text, and English translation based on German audio books. |
| Outcome: | The proposed corpus is the largest resource for German speech recognition and for end-to-end German-to English speech translation. |
SEDAR: a Large Scale French-English Financial Domain Parallel Corpus (2020.lrec-1)
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| Challenge: | Existing approaches for neural machine translation use small amount of data or monolingual data. |
| Approach: | They describe acquisition, preprocessing and characteristics of a large English-French parallel corpus for the financial domain. |
| Outcome: | The proposed corpus contains 8.6 million high quality sentence pairs . the first release of the corpus is available on github. |
JParaCrawl: A Large Scale Web-Based English-Japanese Parallel Corpus (2020.lrec-1)
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| Challenge: | Recent machine translation algorithms rely on parallel corpora, but only some resource-rich language pairs can benefit from them. |
| Approach: | They construct a parallel corpus for English-Japanese, which has 8.7 million sentence pairs . they use a web crawler to automatically align parallel sentences in the corpus . |
| Outcome: | The proposed corpus includes a broader range of domains and can be trained with a pre-trained model. |
Neural Machine Translation for Low-Resourced Indian Languages (2020.lrec-1)
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| Challenge: | Neural machine translation (NMT) is an effective way to convert text to a different language without human involvement. |
| Approach: | They propose to use multihead self-attention along with pre-trained Byte-Pair-Encoded (BPE) and MultiBPE embeddings to develop an efficient machine translation system. |
| Outcome: | The proposed system outperforms Google translator and the existing translators on two of the most morphological rich Indian languages. |
Content-Equivalent Translated Parallel News Corpus and Extension of Domain Adaptation for NMT (2020.lrec-1)
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| Challenge: | Existing methods to train NMT systems with noisy data are not sufficient . a recent increase in foreigners visiting Japan has created a significant information gap . |
| Approach: | They propose a Japanese-English parallel news corpus that is content-equivalent . they extend a domain-adaptation method to train NMT models with clean corpus . |
| Outcome: | The proposed corpus improves translation quality and is more effective than existing methods. |
NMT and PBSMT Error Analyses in English to Brazilian Portuguese Automatic Translations (2020.lrec-1)
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| Challenge: | Recent work proposes neural machine translation (NMT) for Brazilian Portuguese. |
| Approach: | They propose a neural machine translation approach that generates equivalent sentences in target language and source language. |
| Outcome: | The proposed approach outperforms phrase-based statistical machine translation systems for some pairs of languages. |
Evaluation Dataset for Zero Pronoun in Japanese to English Translation (2020.lrec-1)
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| Challenge: | In natural language, we often omit some words that are easily understandable from the context. |
| Approach: | They propose to use a dataset to evaluate whether translation models can resolve zero pronoun problems in Japanese to English translations. |
| Outcome: | The proposed model can resolve the zero pronoun problem in Japanese to English translations. |
Better Together: Modern Methods Plus Traditional Thinking in NP Alignment (2020.lrec-1)
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| Challenge: | a recent study shows that end-to-end systems are not structurally free. |
| Approach: | They propose to use dictionary- and word vector-based baselines to align NPs in the bitext . they argue that alignment of NP's in MT can be improved by using old-fashioned methods . |
| Outcome: | a new study shows that alignment of NPs in the bitext is relevant even in an end-to-end paradigm . the proposed system can be improved by bringing in old-fashioned methods, the authors argue . |
Coursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation (2020.lrec-1)
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| Challenge: | Lectures translation is a case of spoken language translation and there is nil available corpus for this purpose. |
| Approach: | They propose a framework for mining a parallel corpus from publicly available lectures at Coursera . they use machine translation and cosine similarity over continuous-space sentence representations to determine sentence alignments . |
| Outcome: | The proposed framework improves translation performance when used with out-of-domain parallel corpora . it also addresses noise in the mined data, and creates high-quality evaluation splits . |
Being Generous with Sub-Words towards Small NMT Children (2020.lrec-1)
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| Challenge: | In the context of under-resourced neural machine translation, transfer learning from an NMT model trained on a high resource language pair, or from a multilingual NMT (M-NMT) model, has been shown to boost performance to a large extent. |
| Approach: | They propose to use a multilingual NMT model to train on an under-resourced child and to use large sub-word vocabularies to improve performance. |
| Outcome: | The proposed approach involving dynamic vocabularies is both practical and effective on two under-resourced language pairs, i.e. Icelandic-English and Irish-English. |
Document Sub-structure in Neural Machine Translation (2020.lrec-1)
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| Challenge: | Current approaches to machine translation (MT) translate sentences in isolation, disregarding context they appear in, or model context at the level of the full document. |
| Approach: | They propose to include information about the topic of the section within which each sentence is found in a document that is not homogeneous . they use a cache-based model to model the context of the document, instead of translating sentences in isolation . |
| Outcome: | The proposed model incorporates information about the topic of the section within which each sentence is found into a neural model. |
An Evaluation Benchmark for Testing the Word Sense Disambiguation Capabilities of Machine Translation Systems (2020.lrec-1)
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| Challenge: | Lexical ambiguity is one of the many challenging linguistic phenomena involved in translation, i.e., translating an ambiguous word with its correct sense. |
| Approach: | They propose to use training data to measure the sense distributions of a machine translation system to measure lexical ambiguity. |
| Outcome: | The proposed benchmark builds upon the multilingual sense inventory of BabelNet, the multilinguistic neural parsing pipeline TurkuNLP, and the OPUS collection of translated texts from the web. |
MEDLINE as a Parallel Corpus: a Survey to Gain Insight on French-, Spanish- and Portuguese-speaking Authors’ Abstract Writing Practice (2020.lrec-1)
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| Challenge: | Existing corpora are used to train and evaluate machine translation systems, but little information is available about the methods used for producing the corpus, including translation direction. |
| Approach: | They used PubMed and publisher websites to obtain contact information for MEDLINE authors and asked about their abstract writing practices. |
| Outcome: | The authors of MEDLINE articles included in the English/Spanish, English/FR, and English/Portuguese (EN/PT) WMT 2019 test sets reported a response rate of over 20% . |
JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation (2020.lrec-1)
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| Challenge: | Neural machine translation (NMT) requires large parallel corpora for training robust and high quality models. |
| Approach: | They propose a Japanese-specific sequence to sequence pre-training alternative to MASS for NMT . they use Japanese as the source or target language to train their models . |
| Outcome: | The proposed approach can give competitive results over MASS and BRSS, and significantly surpass the individual methods. |
A Post-Editing Dataset in the Legal Domain: Do we Underestimate Neural Machine Translation Quality? (2020.lrec-1)
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Julia Ive, Lucia Specia, Sara Szoc, Tom Vanallemeersch, Joachim Van den Bogaert, Eduardo Farah, Christine Maroti, Artur Ventura, Maxim Khalilov
| Challenge: | Current state-of-the-art in Neural Machine Translation (NMT) has reached remarkable progress, but human evaluations are often judged as having lower quality than top NMT systems. |
| Approach: | They propose to use a machine translation dataset with post-edited high-quality neural machine translation and independent human references to compare the results. |
| Outcome: | The proposed dataset includes 31K tuples including a source sentence, the respective machine translation by a neural machine translation system, and a post-edited version of such translation by professional translator. |
Linguistically Informed Hindi-English Neural Machine Translation (2020.lrec-1)
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| Challenge: | Neural Machine Translation (NMT) is a promising approach to machine translation . lack of parallel training data for Hindi-English is limiting . |
| Approach: | They propose to incorporate linguistic knowledge encoded by Hindi phenomena into a Transformer model to improve the translation performance. |
| Outcome: | The proposed model incorporates linguistic features to improve the translation performance. |
A Test Set for Discourse Translation from Japanese to English (2020.lrec-1)
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| Challenge: | Compared with a previous study on test sets for English-to-French discourse translation, we needed different approaches because Japanese has zero pronouns and represents different senses in different characters. |
| Approach: | They used a test set for Japanese-to-English discourse translation to evaluate the power of context-aware machine translation. |
| Outcome: | The results show that the translation accuracy of Japanese-to-English discourse translation is improved by using context-aware neural machine translation. |
An Analysis of Massively Multilingual Neural Machine Translation for Low-Resource Languages (2020.lrec-1)
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| Challenge: | In this study, we explore massively multilingual low-resource neural machine translation. |
| Approach: | They propose to use Bible translations to train models with up to 1,107 source languages and create multilingual corpora varying the number and relatedness of source languages. |
| Outcome: | The proposed approach is highly language-specific and can be tailored to the source language and its typology. |
TDDC: Timely Disclosure Documents Corpus (2020.lrec-1)
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| Challenge: | TDDC was prepared by manually aligning the sentences from past Japanese and English timely disclosure documents . tens of thousands of original Japanese documents are disclosed every year, but the availability of English disclosure documents is limited. |
| Approach: | They describe the details of the Timely Disclosure Documents Corpus (TDDC) TDDC was prepared by manually aligning the sentences from past Japanese and English timely disclosure documents . |
| Outcome: | The timely disclosure documents corpus (TDDC) was created by aligning sentences from past documents in Japanese and English. |
MuST-Cinema: a Speech-to-Subtitles corpus (2020.lrec-1)
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| Challenge: | Existing methods for subtitling are laborious and costly, says aaron sanchez . he says the current methods are laboriously complex and require manual work . |
| Approach: | They propose to use TED subtitles to build a multilingual speech translation corpus . they propose to annotate existing subtitling corpora with subtitle breaks . |
| Outcome: | The proposed model can be used to segment sentences into subtitles and reduces human work . the proposed model reduces the time and cost of human subtitling tasks . |
On Context Span Needed for Machine Translation Evaluation (2020.lrec-1)
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| Challenge: | a number of common patterns can be observed for context-aware MT evaluation, authors say . document-level evaluations have largely been performed at the sentence level . the definition of what constitutes a "document level" evaluation is still unclear . |
| Approach: | They propose to use a series of surveys to identify the necessary context span . they find common patterns that can be used to draw general guidelines . |
| Outcome: | The proposed evaluations of machine translation systems show that some issues and spans depend on domain and target language. |
A Multilingual Parallel Corpora Collection Effort for Indian Languages (2020.lrec-1)
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| Challenge: | Currently, neural network based approaches for machine translation are data hungry and sentence-level aligned parallel pairs are the currency. |
| Approach: | They propose to build sentence aligned parallel corpora across 10 Indian languages using online sources which have content shared across languages. |
| Outcome: | The proposed corpora significantly extends existing resources that are either not large enough or are restricted to a specific domain (such as health). |
To Case or not to case: Evaluating Casing Methods for Neural Machine Translation (2020.lrec-1)
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| Challenge: | Comparative evaluation of casing methods for Neural Machine Translation . evaluators evaluated methods for tokenisation and word segmentation into subword units . |
| Approach: | They evaluate three main casing methods for Neural Machine Translation to determine optimal handling of capitalisation. |
| Outcome: | The proposed methods are used to handle capitalisation on English-German and English-Turkish datasets. |
The MARCELL Legislative Corpus (2020.lrec-1)
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Tamás Váradi, Svetla Koeva, Martin Yamalov, Marko Tadić, Bálint Sass, Bartłomiej Nitoń, Maciej Ogrodniczuk, Piotr Pęzik, Verginica Barbu Mititelu, Radu Ion, Elena Irimia, Maria Mitrofan, Vasile Păiș, Dan Tufiș, Radovan Garabík, Simon Krek, Andraz Repar, Matjaž Rihtar, Janez Brank
| Challenge: | MARCELL corpus provides a rich and valuable source for further studies and developments in machine learning, cross-lingual terminological data extraction and classification. |
| Approach: | They present the results of the project MARCELL CEF Telecom . they aim to collect and deeply annotate a large comparable corpus of legal documents . |
| Outcome: | The MARCELL corpus includes 7 monolingual sub-corpora containing the body of respective national legislative documents. |
ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts (2020.lrec-1)
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| Challenge: | Existing parallel corpora for patents and scientific texts are not available due to the need for correct alignment and human curation. |
| Approach: | They develop a parallel corpus from the open access Google Patents dataset . they use Hunalign algorithm to align sentences and tokens using the largest 22 languages . |
| Outcome: | The proposed corpus is available in TSV format and with a SQLite database, with complementary information regarding patent metadata. |
Corpora for Document-Level Neural Machine Translation (2020.lrec-1)
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| Challenge: | Document-level machine translation models translate sentences in isolation, but there are three main problems for document-level models. |
| Approach: | They propose to use document-level machine translation to capture discourse dependencies across sentences by considering a document as a whole. |
| Outcome: | The proposed method captures discourse dependencies across sentences by considering a document as a whole. |
OpusTools and Parallel Corpus Diagnostics (2020.lrec-1)
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| Challenge: | Currently OPUS contains 57 released corpora covering over 700 languages and language variants creating more than 70,000 bitexts in the sense of aligned language pairs across all corporata. |
| Approach: | They introduce OpusTools, a package for downloading and processing parallel corpora in OPUS . the package implements tools for accessing compressed data in their archived release format . they show how they can be used in parallel corpus creation and data diagnostics . |
| Outcome: | The proposed tools can be used in parallel corpus creation and data diagnostics. |
Literary Machine Translation under the Magnifying Glass: Assessing the Quality of an NMT-Translated Detective Novel on Document Level (2020.lrec-1)
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| Challenge: | Several studies have demonstrated that translation quality has improved enormously since the emergence of neural machine translation systems. |
| Approach: | They performed a document-level evaluation of the raw NMT output of an entire novel and annotated it in two steps: first all fluency errors, then all accuracy errors. |
| Outcome: | The results show that translation quality has improved enormously since the emergence of neural machine translation systems. |
Handle with Care: A Case Study in Comparable Corpora Exploitation for Neural Machine Translation (2020.lrec-1)
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| Challenge: | Comparable corpora are an important source of potential parallel data, suitable for training data-driven machine translation systems. |
| Approach: | They present a case study on the exploitation of comparable corpora for machine translation. |
| Outcome: | The results show that filtering in terms of alignment thresholds and length-difference outliers has a significant impact on translation quality. |
The FISKMÖ Project: Resources and Tools for Finnish-Swedish Machine Translation and Cross-Linguistic Research (2020.lrec-1)
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Jörg Tiedemann, Tommi Nieminen, Mikko Aulamo, Jenna Kanerva, Akseli Leino, Filip Ginter, Niko Papula
| Challenge: | Finnish and Swedish are the two official languages of Finland. |
| Approach: | They propose to compile a massive corpus of translated material between Finnish and Swedish . they also aim to develop open and freely accessible translation services for those two languages . |
| Outcome: | The project aims to develop open and freely accessible translation services for Finnish and Swedish. |
Multiword Expression aware Neural Machine Translation (2020.lrec-1)
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| Challenge: | Multiword Expressions (MWEs) are a pervasive phenomenon in all natural languages and challenge NLP applications because of their unpredictable morpho-syntactic and lexico--semantic behaviour. |
| Approach: | They propose to use linguistic resources to improve MWE translation and MWE generation by up to 5.09 BLEU points on MWE test sets. |
| Outcome: | The proposed annotation and data augmentation improve translation quality and increase performance by up to 5.09 BLEU points on MWE test sets. |
An Enhanced Mapping Scheme of the Universal Part-Of-Speech for Korean (2020.lrec-1)
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| Challenge: | When mapping a language specific Part-Of-Speech (POS) tag set to the Universal POS tag set (UPOS), it is critical to consider the individual language’s linguistic features and the UPOS definitions. |
| Approach: | They propose to map a Korean Part-Of-Speech (POS) tag set to the Universal POS tag set (UPOS) using a morpheme-based mapping. |
| Outcome: | The proposed mappings are based on the Korean linguistic typology and the substantive definitions of the UPOS categories. |
Finite State Machine Pattern-Root Arabic Morphological Generator, Analyzer and Diacritizer (2020.lrec-1)
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| Challenge: | Using a finite-state morphologizer, we generate and analyze undiacritized Modern Standard Arabic (MSA) words. |
| Approach: | They propose to use a finite-state Arabic Morphologizer to generate and analyze undiacritized Arabic words and diacritize them. |
| Outcome: | The proposed model generates and analyzes undiacritized Modern Standard Arabic (MSA) words and diacritizes them. |
An Unsupervised Method for Weighting Finite-state Morphological Analyzers (2020.lrec-1)
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| Challenge: | Morphological analysis is one of the tasks that have been studied for years. |
| Approach: | They propose a method for weighting a morphological analyzer built using finite state transducers in order to disambiguate its results. |
| Outcome: | The proposed model weights a word2vec model using untagged corpora and captures the semantic meaning of the words. |
Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction (2020.lrec-1)
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| Challenge: | Language-independent tokenisation (LIT) methods that do not require labelled language resources or lexicons have gained popularity because of their compactness and ability to handle unseen or rare words. |
| Approach: | They empirically compare language-independent tokenisation methods with language-specific tokenisation (LST) methods using carefully created lexicons and training resources. |
| Outcome: | The proposed methods outperform LIT and LST on evaluation tasks across eight languages. |
A Supervised Part-Of-Speech Tagger for the Greek Language of the Social Web (2020.lrec-1)
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| Challenge: | Part-of-speech tagging is a fundamental part of NLP, but it is not widely used in unstructured text processing. |
| Approach: | They propose to use part-of-speech tags to extract information from unstructured social text in Greek and a supervised part-off-seech tagger to do so. |
| Outcome: | The proposed method performs better on unstructured microblogging text than existing methods on structured text processing. |
Bag & Tag’em - A New Dutch Stemmer (2020.lrec-1)
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| Challenge: | Current stemmers cannot handle 3rd person singular forms of verbs and many irregular words and conjugations unless a (nearly) brute-force approach is used. |
| Approach: | They propose a novel stemming algorithm that is robust and accurate compared to current stemmers for the Dutch language. |
| Outcome: | The proposed algorithm is more accurate than current stemmers and faster than brute-force-like algorithms. |
Glawinette: a Linguistically Motivated Derivational Description of French Acquired from GLAWI (2020.lrec-1)
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| Challenge: | Glawinette is a derivational lexicon of French that will be used to feed Démonette, a large coverage morphological database. |
| Approach: | They propose to create a derivational lexicon of French that could be used to feed Démonette, a large coverage morphological database. |
| Outcome: | The proposed lexicon could be fed into Démonette, a large coverage morphological database. |
BabyFST - Towards a Finite-State Based Computational Model of Ancient Babylonian (2020.lrec-1)
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| Challenge: | morphological analyzer for Akkadian is not yet available for the extinct language . we present a general finite-state based model for Babylonian that can achieve a coverage of 97.3% and a recall of 93.7% on token level. |
| Approach: | They propose a general finite-state based morphological model for Babylonian that can achieve a coverage of 97.3% and recall up to 93.7% on lemmatization and POS-tagging tasks. |
| Outcome: | The proposed model can achieve coverage and recall of 97.3% on lemmatization and POS-tagging tasks on token level from a transcribed input. |
Morphological Analysis and Disambiguation for Gulf Arabic: The Interplay between Resources and Methods (2020.lrec-1)
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| Challenge: | Despite advances in the field of natural language processing, many dialectal Arabic varieties are lagging behind . despite advances in NLP, many Arabic dialects are considered under-resourced . |
| Approach: | They propose a full morphological analysis and disambiguation system for Gulf Arabic . they use existing state-of-the-art morphology tools to investigate the effects of different data sizes and combinations of morphologists. |
| Outcome: | The proposed system improves on the existing system for Arabic . it is based on a set of data sizes and combinations of morphological analyzers . |
Wikinflection Corpus: A (Better) Multilingual, Morpheme-Annotated Inflectional Corpus (2020.lrec-1)
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| Challenge: | Inflectional corpora with annotated morpheme boundaries are scarce in the NLP community . a generated, multilingual inflectional lexicon with morphological features is not as good as UniMorph's . |
| Approach: | They evaluate a multilingual inflectional corpus with morpheme boundaries from the English Wiktionary and the UniMorph project's inflection corpus. |
| Outcome: | The generated Wikinflection corpus is not as good as UniMorph's, but extracts significant amount of words from the intersection of the two corpora. |
Introducing a Large-Scale Dataset for Vietnamese POS Tagging on Conversational Texts (2020.lrec-1)
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| Challenge: | POS taggers are trained on informal texts which contain many informal inputs such as acronyms, abbreviations, out-of-vocabulary words, etc. |
| Approach: | They propose a large-scale human-labeled dataset for the Vietnamese POS tagging task on conversational texts and develop an annotation guideline to manually annotate 16.310K sentences using this guideline. |
| Outcome: | The proposed tagging scheme achieved 93.36% accuracy score and higher than the model with handcrafted features and fine-tuning BERT. |
UniMorph 3.0: Universal Morphology (2020.lrec-1)
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Arya D. McCarthy, Christo Kirov, Matteo Grella, Amrit Nidhi, Patrick Xia, Kyle Gorman, Ekaterina Vylomova, Sabrina J. Mielke, Garrett Nicolai, Miikka Silfverberg, Timofey Arkhangelskiy, Nataly Krizhanovsky, Andrew Krizhanovsky, Elena Klyachko, Alexey Sorokin, John Mansfield, Valts Ernštreits, Yuval Pinter, Cassandra L. Jacobs, Ryan Cotterell, Mans Hulden, David Yarowsky
| Challenge: | Explicit modeling of morphology has demonstrable benefits for language modeling, speech recognition, word embedding and keyword search. |
| Approach: | They propose a language-independent feature schema for rich morphological annotation and a type-level resource for annotated data in diverse languages. |
| Outcome: | The proposed schema has been improved to make it more complete and correct, and adds 66 new languages and parts of speech for 12 languages. |
Building the Spanish-Croatian Parallel Corpus (2020.lrec-1)
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| Challenge: | The first unidirectional parallel corpus spanish-croatia was built at the Faculty of Humanities and Social Sciences of the University of Zagreb. |
| Approach: | They describe the building of the first Spanish-Croatian unidirectional parallel corpus at the Faculty of Humanities and Social Sciences of the University of Zagreb. |
| Outcome: | The proposed corpus is a bilingual unidirectional (SpanishCroatian) parallel corpus . it contains 11 Spanish novels and their translations to Croatian done by six translators . |
DerivBase.Ru: a Derivational Morphology Resource for Russian (2020.lrec-1)
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| Challenge: | DerivBase.Ru is a high-coverage derivational morphology resource for Russian language that can be used for many tasks such as paraphrases and plagiarism detection. |
| Approach: | They propose a rule-based framework for deriving Russian words using a derivational morphology resource called DerivBase.Ru. |
| Outcome: | The proposed framework can be used to derivate words from a dictionary in Russian and German. |
Morfessor EM+Prune: Improved Subword Segmentation with Expectation Maximization and Pruning (2020.lrec-1)
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| Challenge: | Subword segmentation is a standard preprocessing step in many neural approaches to natural language processing. |
| Approach: | They propose to train a unigram subword model using a recursive algorithm and lexicon pruning algorithm. |
| Outcome: | The proposed method improves on the original training algorithm and improves morphological segmentation accuracy. |
Machine Learning and Deep Neural Network-Based Lemmatization and Morphosyntactic Tagging for Serbian (2020.lrec-1)
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| Challenge: | The training of new tagger models for Serbian is motivated by the enhancement of the existing tagset with the grammatical category of a gender. |
| Approach: | They propose to use TreeTagger and spaCy taggers to train new Serbian tagger models and to align Serbian morphological dictionaries with the grammatical category of a gender. |
| Outcome: | The proposed models achieve 98% PoS-tagging precision per token, and the annotated dataset will be published. |
Fine-grained Morphosyntactic Analysis and Generation Tools for More Than One Thousand Languages (2020.lrec-1)
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| Challenge: | Using morphosyntactic tools, we train and distribute tools for approximately one thousand languages. |
| Approach: | They train and distribute morphosyntactic tools for approximately one thousand languages. |
| Outcome: | The results show that the tools generalize well across rare and common forms alike. |
Cairo Student Code-Switch (CSCS) Corpus: An Annotated Egyptian Arabic-English Corpus (2020.lrec-1)
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| Challenge: | Code-switching is a phenomenon commonly observed in the Arabicspeaking world . there is still a huge gap in the available resources and NLP applications . |
| Approach: | They propose a corpus of Egyptian- Arabic code-switch speech data that is fully tokenized, lemmatized and annotated for part-of-speech tags. |
| Outcome: | The proposed corpus of Egyptian- Arabic code-switch speech data is fully tokenized, lemmatized and annotated for part-of-speech tags. |
Getting More Data for Low-resource Morphological Inflection: Language Models and Data Augmentation (2020.lrec-1)
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| Challenge: | Morphological inflection is the process that generates the word form given its lexeme and morphological properties. |
| Approach: | They propose to use language models and data augmentation to improve morphological inflection without annotating more data. |
| Outcome: | The proposed model improves by 1.5% with the langauge model and by 9% with the data augmentation. |
Visual Modeling of Turkish Morphology (2020.lrec-1)
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| Challenge: | Currently, there are three publicly accessible morphological analyzers for Turkish . |
| Approach: | They propose to make modeling easier and more maintainable by using diagramming tools and automating much of the code generation. |
| Outcome: | The proposed model can be easily maintained and the code generation automated. |
Kvistur 2.0: a BiLSTM Compound Splitter for Icelandic (2020.lrec-1)
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| Challenge: | Compounding is highly productive in Icelandic, and new compounds are constantly being created. |
| Approach: | They propose a character-based biLSTM model for splitting Icelandic compound words . the model learns how to split compound words into two parts . |
| Outcome: | The proposed model outperforms other methods on a corpus of manually split word forms. |
Morphological Segmentation for Low Resource Languages (2020.lrec-1)
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Justin Mott, Ann Bies, Stephanie Strassel, Jordan Kodner, Caitlin Richter, Hongzhi Xu, Mitchell Marcus
| Challenge: | a new corpus of annotated morphological data is described for the DARPA LORELEI Program . the data is annotating 9 low resource languages and root information for 7 of the languages . |
| Approach: | This paper describes a new morphology resource created by Linguistic Data Consortium and the University of Pennsylvania for the DARPA LORELEI Program. |
| Outcome: | The annotated corpus provides a gold standard for unsupervised morphological segmenters and analyzers . the language-specific annotation guidelines were language-independent, but included morphology paradigms and other specifications. |
CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data (2020.lrec-1)
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Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, Edouard Grave
| Challenge: | Pre-training text representations have led to significant improvements in many areas of natural language processing. |
| Approach: | They propose a pipeline to extract monolingual datasets from Common Crawl . pipeline follows data processing introduced in fastText that deduplicates documents . |
| Outcome: | The proposed pipeline performs standard document deduplication and language identification similar to the pipeline introduced in fastText and a filtering step to select documents close to high quality corpora like Wikipedia. |
On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning (2020.lrec-1)
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| Challenge: | Cross-lingual word embeddings are vector representations of words in different languages where words with similar meaning are represented by similar vectors, regardless of the language. |
| Approach: | They propose to evaluate multiple cross-lingual word embedding models and compare their strengths and limitations to evaluate their effectiveness. |
| Outcome: | The proposed models perform well with noisy text and language pairs with major differences. |
Building an English-Chinese Parallel Corpus Annotated with Sub-sentential Translation Techniques (2020.lrec-1)
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| Challenge: | a recent study shows that human translators often resort to different non-literal translation techniques besides literal translation . however, they receive less attention in developing natural language processing (NLP) applications. |
| Approach: | They propose to have a better semantic control of extracting paraphrases from bilingual parallel corpora. |
| Outcome: | The proposed method can automatically recognize different non-literal translation techniques . the results confirm the hypothesis of the proposed method . |
Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection (2020.lrec-1)
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Joakim Nivre, Marie-Catherine de Marneffe, Filip Ginter, Jan Hajič, Christopher D. Manning, Sampo Pyysalo, Sebastian Schuster, Francis Tyers, Daniel Zeman
| Challenge: | Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages. |
| Approach: | They describe version 2 of the universal guidelines and discuss major changes from UD v1 to UD 2 . they propose a morphological layer, a syntactic layer and a word segmentation layer . |
| Outcome: | The proposed treebanks are available for 90 languages and have been updated to meet the needs of multilingual parsers and researchers. |
EMPAC: an English–Spanish Corpus of Institutional Subtitles (2020.lrec-1)
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| Challenge: | EMPAC is a collection of subtitles in English and Spanish for videos from the EuropeanParliament’s Multimedia Centre. |
| Approach: | The EuroparlTV Multimedia Parallel Corpus (EMPAC) is a collection of subtitles in English and Spanish for videos from the EuropeanParliament’s Multimedia Centre. |
| Outcome: | The corpus covers a time span from 2009 to 2017, and is made up of 4,000 texts amounting to two and half millions of tokens for every language, corresponding to approximately 280 hours of video. |
Cross-Lingual Word Embeddings for Turkic Languages (2020.lrec-1)
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| Challenge: | Existing techniques to align monolingual embeddings are difficult to use because of low resources. |
| Approach: | They propose to use existing techniques to align monolingual embedding spaces for Turkic, Uzbek, Azeri, Kazakh and Kyrgyz languages. |
| Outcome: | The proposed techniques outperform existing techniques on bilingual dictionaries and an extrinsic task. |
How Universal are Universal Dependencies? Exploiting Syntax for Multilingual Clause-level Sentiment Detection (2020.lrec-1)
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| Challenge: | a new method for clause-level sentiment detection is proposed for multilingual use cases. |
| Approach: | They propose a pipeline method that makes the most of syntactic structures based on Universal Dependencies. |
| Outcome: | The proposed method achieves high precision in sentiment detection for 17 languages . it avoids machine-learning approaches that may cause obstacles to its use cases . |
Multilingual Culture-Independent Word Analogy Datasets (2020.lrec-1)
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| Challenge: | In text processing, deep neural networks use word embeddings as an input. |
| Approach: | They propose to use benchmark datasets to compare the quality of word embeddings in text processing . they use a word analogy task in Croatian, English, Estonian, Finnish, Latvian, Lithuanian, Russian, Slovenian, and Swedish . |
| Outcome: | The proposed datasets are culturally independent and cross-lingual for the languages used. |
GeBioToolkit: Automatic Extraction of Gender-Balanced Multilingual Corpus of Wikipedia Biographies (2020.lrec-1)
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| Challenge: | a tool for extracting multilingual parallel corpora at sentence level with document and gender information from Wikipedia biographies. |
| Approach: | They propose a tool for extracting multilingual parallel corpora at sentence level with document and gender information from Wikipedia biographies. |
| Outcome: | The proposed tool extracts a corpus of 2,000 sentences in English, Spanish and Catalan. |
SpiCE: A New Open-Access Corpus of Conversational Bilingual Speech in Cantonese and English (2020.lrec-1)
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| Challenge: | SpiCE is a corpus of conversational Cantonese-English bilingual speech recorded in Vancouver, Canada . the corpus includes high-quality recordings of 34 early bilinguals in both English and Cantoneses . |
| Approach: | They describe the design, collection, orthographic transcription, and phonetic annotation of SpiCE . the corpus includes high-quality recordings of 34 early bilinguals in both English and Cantonese . |
| Outcome: | The SpiCE corpus includes high-quality recordings of 34 early bilinguals in both English and Cantonese . the corpus will promote bilingualism research for a typologically distinct pair of languages . |
Identifying Cognates in English-Dutch and French-Dutch by means of Orthographic Information and Cross-lingual Word Embeddings (2020.lrec-1)
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| Challenge: | Existing methods to identify cognate pairs in English-Dutch and French-Dutsch combine orthographic information with cross-lingual word embeddings. |
| Approach: | They combine traditional orthographic information with cross-lingual word embeddings to identify cognate pairs in English-Dutch and French-Dutsch. |
| Outcome: | The proposed classifier achieves good results on the basis of orthographic information but improves by including semantic information in the form of cross-lingual word embeddings. |
Lexicogrammatic translationese across two targets and competence levels (2020.lrec-1)
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| Challenge: | a specificity of translations with English as a source language produced by students and professional translators is investigated by genre-comparable data from a number of parallel and comparable corpora. |
| Approach: | They propose to use genre-comparable data to explore the specificity of translations . they use a set of human-interpretable lexicogrammatic translationese indicators . |
| Outcome: | The proposed feature set can reliably distinguish translations and non-translations regardless of the language pair and translation variety. |
UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages (2020.lrec-1)
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| Challenge: | Sentiment lexica are vital for sentiment analysis in absence of document-level annotations . linguistic resources are limited for at least a few hundred languages, putting them at risk of extinction . |
| Approach: | They introduce UniSent universal sentiment lexica for 1000+ languages . they use a Bible corpus to project sentiment information from English to other languages based on Twitter data . |
| Outcome: | The proposed method mitigates domain mismatch between Bible and Twitter by using embeddings . it compares to other sentiment seeding methods in a subset of languages with ground truth available . |
CanVEC - the Canberra Vietnamese-English Code-switching Natural Speech Corpus (2020.lrec-1)
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| Challenge: | Using monolingual tools, code-switching is a problem in the natural language processing community. |
| Approach: | They introduce the Canberra Vietnamese-English Code-switching corpus (CanVEC) which is an original corpus of mixed speech annotated with language information, part of speech tags and Vietnamese translations. |
| Outcome: | The proposed corpus was annotated with language information, part of speech tags and Vietnamese translations using pipelining and monolingual toolkits. |
A Spelling Correction Corpus for Multiple Arabic Dialects (2020.lrec-1)
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| Challenge: | Arabic dialects are non-standard varieties of Arabic commonly spoken across the Arab world, but lack standard orthographies. |
| Approach: | They present a corpus of 10,000 sentences from five Arabic city dialects represented in the Conventional Orthography for Dialectal Arabic (CODA) they use a bootstrapping technique to speed up annotation and compare similarity between dialects before and after CODA annotation. |
| Outcome: | The proposed method speeds up the annotation process and shows similarity between the dialects before and after CODA annotation. |
A Dataset for Multi-lingual Epidemiological Event Extraction (2020.lrec-1)
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| Challenge: | Using the Web, we propose a corpus for information extraction and text classification. |
| Approach: | They propose to use a corpus for information extraction and natural language processing (NLP) tasks such as text classification. |
| Outcome: | The proposed corpus can be used for information extraction and natural language processing tasks such as text classification. |
Swiss-AL: A Multilingual Swiss Web Corpus for Applied Linguistics (2020.lrec-1)
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| Challenge: | Swiss-AL is a multilingual web corpus for Applied Linguistics that supports data-based and data-driven research on societal and political discourses in Switzerland. |
| Approach: | They propose a multilingual Swiss web corpus for Applied Linguistics that supports data-based research on societal and political discourses in Switzerland. |
| Outcome: | The Swiss Web Corpus for Applied Linguistics (SWS) is a multilingual collection of texts from selected web sources. |
Analysis of GlobalPhone and Ethiopian Languages Speech Corpora for Multilingual ASR (2020.lrec-1)
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| Challenge: | Using global phone data, we can develop multilingual speech recognition systems in yet unsupported languages. |
| Approach: | They analyze phonetic overlaps between GlobalPhone and Ethiopian speech corpora to develop multilingual Automatic Speech Recognition system for the Ethiopian languages. |
| Outcome: | The proposed system will be able to support three different languages and have morphological complexity. |
Multilingualization of Medical Terminology: Semantic and Structural Embedding Approaches (2020.lrec-1)
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| Challenge: | Existing methods for multilingual terminology curation are limited as they do not fit the term within existing terminology. |
| Approach: | They propose a method to encode the structural property of a term by aligning embeddings using graph convolutional networks trained from separate languages. |
| Outcome: | The proposed method can encode the structural property of a term by aligning embeddings using graph convolutional networks trained from separate languages. |
Large Vocabulary Read Speech Corpora for Four Ethiopian Languages: Amharic, Tigrigna, Oromo and Wolaytta (2020.lrec-1)
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Solomon Teferra Abate, Martha Yifiru Tachbelie, Michael Melese, Hafte Abera, Tewodros Abebe, Wondwossen Mulugeta, Yaregal Assabie, Million Meshesha, Solomon Afnafu, Binyam Ephrem Seyoum
| Challenge: | Automatic Speech Recognition (ASR) is one of the most important technologies to support spoken communication in modern life. |
| Approach: | They have developed four large speech corpora for four Ethiopian languages . they have word error rates of 37.65%, 31.03%, 38.02%, 33.89% for each language . |
| Outcome: | The proposed corpora achieve word error rates of 37.65%, 31.03%, 38.02%, 33.89% for Amharic, Tigrigna, Oromo and Wolaytta. |
Incorporating Politeness across Languages in Customer Care Responses: Towards building a Multi-lingual Empathetic Dialogue Agent (2020.lrec-1)
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| Challenge: | Qualitative and quantitative analysis shows that our proposed model can converse in both the languages and the information shared between the languages helps in improving the performance of the overall system. |
| Approach: | They propose a deep learning framework that can handle different languages and incorporate courteous behaviour in generic customer care responses in a multi-lingual scenario. |
| Outcome: | The proposed model can converse in both languages and the information shared between the languages helps in improving the overall performance of the system. |
WikiBank: Using Wikidata to Improve Multilingual Frame-Semantic Parsing (2020.lrec-1)
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| Challenge: | Frame-semantic annotations exist for a tiny fraction of the world’s languages, however, Wikidata provides a common, distant supervision signal for semantic parsers. |
| Approach: | They propose a multilingual resource with partial semantic dependency structures that can be used to extend pre-existing resources rather than creating new man-made resources from scratch. |
| Outcome: | The proposed resource can be used to augment pre-existing resources or reduce the annotation effort for low-resource languages. |
Multilingual Corpus Creation for Multilingual Semantic Similarity Task (2020.lrec-1)
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| Challenge: | Existing monolingual corpora are limited for semantic similarity tasks . a major factor affecting the success of deep neural networks is the availability of large and good quality corpors. |
| Approach: | They propose a semi-automated framework to create a multilingual corpus for a semantic similarity task. |
| Outcome: | The proposed framework can be applied to government, insurance, banking domains provided a bilingual website exists. |
CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus (2020.lrec-1)
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| Challenge: | Existing datasets involve language pairs with English as source language, are low resource or lack labeled data. |
| Approach: | They propose a multilingual speech-to-text translation corpus from 11 languages into English . they provide empirical evidence of the quality of the data and provide initial benchmarks . |
| Outcome: | The proposed model is the first end-to-end multilingual model for spoken language translation. |
A Visually-Grounded Parallel Corpus with Phrase-to-Region Linking (2020.lrec-1)
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| Challenge: | Existing multimodal corpora lack the ability to be used in multilingual or non-English scenarios. |
| Approach: | They extend a Flickr30k Entities image-caption dataset with Japanese translations to provide a multilingual corpus. |
| Outcome: | The proposed dataset is the first multilingual image-caption dataset with Japanese translations. |
Multilingual Dictionary Based Construction of Core Vocabulary (2020.lrec-1)
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| Challenge: | Existing methods for core vocabulary lists for multiple applications are lacking coverage in sparse dictionaries . we propose a new method for definition and construction of core vocabulary sets based on coverage in dictionary dictionaria . |
| Approach: | They propose a functional definition and construction method for core vocabulary sets based on relative coverage of a target concept in bilingual dictionaries. |
| Outcome: | The proposed method achieves high overlap with existing vocabulary lists . it uses a cognate prediction method to recover missing coverage of the vocabulary . |
Common Voice: A Massively-Multilingual Speech Corpus (2020.lrec-1)
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Rosana Ardila, Megan Branson, Kelly Davis, Michael Kohler, Josh Meyer, Michael Henretty, Reuben Morais, Lindsay Saunders, Francis Tyers, Gregor Weber
| Challenge: | Common Voice is a massively-multilingual collection of transcribed speech intended for speech technology research and development. |
| Approach: | They propose to use Mozilla’s DeepSpeech Speech-to-Text toolkit to perform multilingual automatic speech recognition experiments. |
| Outcome: | The proposed corpus is the largest in the public domain for speech recognition, both in terms of hours and languages. |
Massively Multilingual Pronunciation Modeling with WikiPron (2020.lrec-1)
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Jackson L. Lee, Lucas F.E. Ashby, M. Elizabeth Garza, Yeonju Lee-Sikka, Sean Miller, Alan Wong, Arya D. McCarthy, Kyle Gorman
| Challenge: | WikiPron is an open-source command-line tool for extracting pronunciation data from Wiktionary . the tool generates a database of 1.7 million pronunciations from 165 languages . |
| Approach: | They propose a command-line tool for extracting pronunciation data from Wiktionary . they use it to generate a database of 1.7 million pronunciations from 165 languages . |
| Outcome: | The proposed software generates a database of pronunciations for 165 languages . the proposed model is then validated by a grapheme-to-phoneme model . |
HELFI: a Hebrew-Greek-Finnish Parallel Bible Corpus with Cross-Lingual Morpheme Alignment (2020.lrec-1)
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Anssi Yli-Jyrä, Josi Purhonen, Matti Liljeqvist, Arto Antturi, Pekka Nieminen, Kari M. Räntilä, Valtter Luoto
| Challenge: | Parallel editions of Bible translations have existed for 1,800 years, but there is a steadily growing interest to attach a fine-grained alignment to the numerous translations of the Bible and other parallel texts. |
| Approach: | They propose to produce an openly shareable, fine-grained alignment for parallel Bibles using only freely available text editions and annotations. |
| Outcome: | The proposed dataset contains the source texts and translations, morphological analyses and cross-lingual morpheme alignments. |
ArzEn: A Speech Corpus for Code-switched Egyptian Arabic-English (2020.lrec-1)
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| Challenge: | a corpus of Arabic-English code-switching (CS) spontaneous speech is collected in an Egyptian university soundproof room . the language in Egypt is rather complex and poses many challenges to natural language processing (NLP) |
| Approach: | They present an Egyptian Arabic-English code-switching (CS) spontaneous speech corpus. |
| Outcome: | The proposed corpus is designed to be used in automatic speech recognition systems . it provides a useful resource for analyzing the CS phenomenon from linguistic, sociological, and psychological perspectives. |
Cross-lingual Named Entity List Search via Transliteration (2020.lrec-1)
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| Challenge: | a common way to adapt out-of-vocabulary words is a challenge in cross-lingual tasks . intrinsic evaluation, i.e comparison to a single gold standard, might not be appropriate in the task of transliteration due to its high variability. |
| Approach: | They propose to train Transformer-based multilingual transliteration models on 6 high- and 4 less-resourced languages and compare them with bilingual models. |
| Outcome: | The proposed model outperforms bilingual models on less-resourced languages. |
Serial Speakers: a Dataset of TV Series (2020.lrec-1)
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| Challenge: | a new dataset of 155 episodes from popular american TV series is available to researchers . the dataset includes annotations for every speech turn (boundaries, speaker) and scene boundary . |
| Approach: | They provide annotated dataset of 155 episodes from three popular american TV serials . they publicly release annotations for every speech turn (boundaries, speaker) and scene boundary . |
| Outcome: | The dataset includes 155 episodes from three popular american TV serials: “Breaking Bad”, “Game of Thrones” and “House of Cards”. |
Image Position Prediction in Multimodal Documents (2020.lrec-1)
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| Challenge: | Existing multimodal tasks allow machines to understand images by describing or being asked in natural language. |
| Approach: | They propose a task that predicts the positions of images in a given document . they use a dataset of 66K multimodal documents with 320K images from Wikipedia . |
| Outcome: | The proposed task outperforms baselines while the performance is far from human. |
Visual Grounding Annotation of Recipe Flow Graph (2020.lrec-1)
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Taichi Nishimura, Suzushi Tomori, Hayato Hashimoto, Atsushi Hashimoto, Yoko Yamakata, Jun Harashima, Yoshitaka Ushiku, Shinsuke Mori
| Challenge: | Existing studies have ground visual observations with procedural texts with graphs to understand which objects are aligned with textual descriptions. |
| Approach: | They propose to provide visual grounding annotations to recipe flow graphs by adding bounding boxes to image sequences of recipes and annotating two types of event attributes with each bounding box. |
| Outcome: | The proposed dataset gives visual grounding with workflow’s contextual information between procedural text and visual observation in an indirect manner. |
Building a Multimodal Entity Linking Dataset From Tweets (2020.lrec-1)
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| Challenge: | Entity linking is a task that aims at associating an entity mention with a unique entity in a knowledge base. |
| Approach: | They propose a method to quasi-automatically build annotated datasets to evaluate methods on the Entity Linking task. |
| Outcome: | The proposed method builds annotated datasets of tweets with ambiguous mentions and a Twitter KB defining the entities. |
A Multimodal Educational Corpus of Oral Courses: Annotation, Analysis and Case Study (2020.lrec-1)
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Salima Mdhaffar, Yannick Estève, Antoine Laurent, Nicolas Hernandez, Richard Dufour, Delphine Charlet, Geraldine Damnati, Solen Quiniou, Nathalie Camelin
| Challenge: | a corpus of spontaneous speech is being developed for educational use . the dataset will be freely available to the research community . |
| Approach: | They propose to use a French speech educational corpus to explore synchronous speech transcription and application in teaching situations. |
| Outcome: | The proposed corpus includes 10 hours of lectures, manually transcribed and segmented . the dataset will be freely available to the research community . |
Annotating Event Appearance for Japanese Chess Commentary Corpus (2020.lrec-1)
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| Challenge: | Recent studies show that text and non-text data are not always a “true” pair. |
| Approach: | They propose "Event Appearance" labels that show the relationship between events mentioned in texts and those happening in the real world. |
| Outcome: | The proposed labels show the relationship between events mentioned in texts and those happening in the real world. |
Offensive Video Detection: Dataset and Baseline Results (2020.lrec-1)
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| Challenge: | a large number of social media platforms discourage users from publishing offensive content . however, there is no method to detect offensive content on these platforms due to the high volume of publications. |
| Approach: | They propose to use text-based machine learning to detect offensive content on different platforms . they use word embedding with Deep Learning classifiers to perform best results . |
| Outcome: | The proposed methods outperform Classic and Deep Learning classifiers in Portuguese and CNN architectures in other features. |
Adding Gesture, Posture and Facial Displays to the PoliModal Corpus of Political Interviews (2020.lrec-1)
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| Challenge: | a corpus of face-to-face interviews is annotated with facial displays, hand gestures and body posture. |
| Approach: | They introduce a multimodal corpus on top of transcribed face-to-face interviews that presents the annotation of facial displays, hand gestures and body posture. |
| Outcome: | The proposed corpus is extracted from a larger corpus of 56 face-to-face interviews (14 hours) the annotations include facial displays, hand gestures and body posture. |
E:Calm Resource: a Resource for Studying Texts Produced by French Pupils and Students (2020.lrec-1)
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| Challenge: | É:CALM resource is constructed from handwritten texts produced in a variety of usual contexts of teaching. |
| Approach: | They describe the whole data processing established for digitizing and standardizing such manuscripts in order to provide an ecological resource for NLP, corpus linguistics and POS. |
| Outcome: | The E:CALM resource is constructed from hand-written texts produced in a variety of usual contexts of teaching. |
Introducing MULAI: A Multimodal Database of Laughter during Dyadic Interactions (2020.lrec-1)
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| Challenge: | Many theories exist about the origin and function of laughter, however, most theorists agree that it plays a major role in day to day social interactions between humans. |
| Approach: | They use the Multimodal Laughter during Interaction database to study laughter expressive patterns . they use acoustic laughter properties and annotated humour ratings to explore the link between humor and laughter properties. |
| Outcome: | The proposed database combines 601 laughs, 168 speech-laughs and 538 on- or offset respirations with other data rarely captured by other laughter databases. |
The Connection between the Text and Images of News Articles: New Insights for Multimedia Analysis (2020.lrec-1)
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| Challenge: | a case study of text and images reveals the inadequacy of simplistic assumptions about their connection and interplay. |
| Approach: | They propose to use a case study to analyze 1000 flood-related news articles . they find that articles cluster into seven categories related to different topical aspects of flooding . |
| Outcome: | The results show that flood-related news articles do not consistently report on a single, currently unfolding flooding event. |
LifeQA: A Real-life Dataset for Video Question Answering (2020.lrec-1)
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Santiago Castro, Mahmoud Azab, Jonathan Stroud, Cristina Noujaim, Ruoyao Wang, Jia Deng, Rada Mihalcea
| Challenge: | Existing video question answering datasets consist of movies and TV shows, but they are not representative of our day-to-day lives. |
| Approach: | They propose a benchmark dataset for video question answering that focuses on day-to-day situations. |
| Outcome: | The proposed dataset analyzes the challenging but realistic aspects of LifeQA . it consists of video clips and over 2.3k multiple-choice questions . |
A Domain-Specific Dataset of Difficulty Ratings for German Noun Compounds in the Domains DIY, Cooking and Automotive (2020.lrec-1)
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| Challenge: | a dataset with difficulty ratings for 1,030 closed noun compounds is presented . authors use a simple compound splitter to identify compound types in domain-specific texts . |
| Approach: | They present a German closed noun compound dataset with difficulty ratings . they used a simple compound splitter to identify compounds in texts . |
| Outcome: | The proposed dataset has difficulty ratings for 1,030 closed noun compounds extracted from domain-specific texts for do-it-ourself, cooking and automotive. |
All That Glitters is Not Gold: A Gold Standard of Adjective-Noun Collocations for German (2020.lrec-1)
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| Challenge: | Using the GerCo dataset, we identify adjective-noun collocations in German and compare them with statistical associations measures. |
| Approach: | They present a GerCo dataset of adjective-noun collocations for German, such as alter Freund ‘old friend’ and tiefe Liebe ‘deep love’. |
| Outcome: | The GerCo dataset contains 4,732 positive and negative instances of collocations and covers all 16 semantic classes of adjectives defined in the German wordnet GermaNet. |
Variants of Vector Space Reductions for Predicting the Compositionality of English Noun Compounds (2020.lrec-1)
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| Challenge: | Existing approaches to predict the degree of compositionality of noun compounds are based on comparing compounds and their constituents within a vector space and using distributional similarity as a proxy to predict their degree of semantic relatedness. |
| Approach: | They propose to use distributional similarity as a proxy to predict the semantic relatedness between the compounds and their constituents as the compound’s degree of compositionality. |
| Outcome: | The proposed methods are most successful and stable in terms of dimensionality and part-of-speech reductions. |
Varying Vector Representations and Integrating Meaning Shifts into a PageRank Model for Automatic Term Extraction (2020.lrec-1)
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| Challenge: | a comparative study for automatic term extraction from domain-specific language using a PageRank graph algorithm with different edge-weighting methods. |
| Approach: | They propose to use a PageRank algorithm to extract automatic terms from domain-specific language using different edge-weighting methods. |
| Outcome: | The proposed model is compared with a PageRank model with different edge-weighting methods. |
Rigor Mortis: Annotating MWEs with a Gamified Platform (2020.lrec-1)
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| Challenge: | gamification of the platform should be improved, in order to attract and retain more players. |
| Approach: | They propose to use a gamified crowdsourcing platform to evaluate the intuition of speakers and then train them to annotate multi-word expressions in French corpora. |
| Outcome: | The proposed platform evaluates the speakers' intuition and trains them to annotate multi-word expressions in French corpora. |
A Multi-word Expression Dataset for Swedish (2020.lrec-1)
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| Challenge: | Existing data on compositionality of multi-word expressions is limited and only available for high resource languages. |
| Approach: | They present a set of Swedish multi-word expressions annotated with degree of compositionality . they also consider syntactically complex constructions and publish a formal specification of each expression . |
| Outcome: | The proposed dataset includes 96 Swedish multi-word expressions with degree of compositionality. |
A Joint Approach to Compound Splitting and Idiomatic Compound Detection (2020.lrec-1)
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| Challenge: | Compounding is a common word-formation process in Germanic languages . high productivity and low corpus frequency of compounds increase vocabulary size . |
| Approach: | They develop a deep learning-based approach to noun compound splitting and idiomatic compound detection for the German language. |
| Outcome: | The proposed approach outperforms the current state of the art in noun compound splitting and idiomatic compound detection for the German language. |
Dedicated Language Resources for Interdisciplinary Research on Multiword Expressions: Best Thing since Sliced Bread (2020.lrec-1)
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| Challenge: | Multiword expressions are challenging for disciplines like NLP, psycholinguistics and second language acquisition due to their more or less fixed character. |
| Approach: | They propose to develop tools and language resources that are crucial for multifaceted research. |
| Outcome: | The proposed tools and language resources are crucial for this kind of multifaceted research. |
Detecting Multiword Expression Type Helps Lexical Complexity Assessment (2020.lrec-1)
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| Challenge: | Multiword expressions (MWEs) represent lexemes that should be treated as single lexical units due to their idiosyncratic nature. |
| Approach: | They re-annotate a complex word identification shared task 2018 dataset . they find that a lexical complexity assessment system benefits from the information . |
| Outcome: | The proposed dataset provides valuable information for the text simplification community. |
Introducing RONEC - the Romanian Named Entity Corpus (2020.lrec-1)
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| Challenge: | Named Entity Corpus is a free, open-source resource that contains annotated named entities in copy-right free text. |
| Approach: | They present RONEC - the Named Entity Corpus for the Romanian language . it contains over 26000 entities in 5000 annotated sentences belonging to 16 classes . |
| Outcome: | The free, open-source resource contains over 26000 entities in 5000 annotated sentences, belonging to 16 distinct classes. |
A Semi-supervised Approach for De-identification of Swedish Clinical Text (2020.lrec-1)
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| Challenge: | An abundance of electronic health records (EHRs) is produced every day within healthcare. |
| Approach: | They propose a semi-supervised method for automatically creating high-quality training data for de-identification using annotated data for training and annotations that are costly in time and human resources. |
| Outcome: | The proposed method improves recall from 84.75% to 89.20% without sacrificing precision to the same extent, dropping from 95.73% to 94.20%. |
A Chinese Corpus for Fine-grained Entity Typing (2020.lrec-1)
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| Challenge: | Existing datasets for fine-grained entity typing are limited to English . a corpus of 4,800 mentions is manually labeled with free-form entity types . |
| Approach: | They propose a Chinese fine-grained entity typing task that uses crowdsourcing . they categorize each mention into 10 general types and use a large tag set to predict open set of types . |
| Outcome: | The proposed dataset contains 4,800 mentions manually labeled in Chinese . it also categorizes all the fine-grained types into 10 general types . |
Czech Historical Named Entity Corpus v 1.0 (2020.lrec-1)
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| Challenge: | a lack of annotated historical data for named entity recognition is an obstacle to research in this area. |
| Approach: | They propose to create an annotated corpus for named entity recognition in historical documents . they define domain-specific named entity types and create an annotation manual . |
| Outcome: | The proposed corpus is available for research and is available to download . it is the first annotated historical corpus for named entity recognition (NER) |
CodE Alltag 2.0 — A Pseudonymized German-Language Email Corpus (2020.lrec-1)
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| Challenge: | unauthorized use of social media content as a data resource is often neglected . data privacy concerns are often overlooked in NLP research . |
| Approach: | They propose an algorithm for the protection of personal data via pseudonymization by automatically recognizing privacy-sensitive stretches of text in UGC. |
| Outcome: | The proposed algorithm protects personal data via pseudonymization on two hitherto non-anonymized German-language email corpora. |
A Dataset of German Legal Documents for Named Entity Recognition (2020.lrec-1)
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| Challenge: | a dataset developed for Named Entity Recognition in German federal court decisions is available under a CC-BY 4.0 license. |
| Approach: | They describe a dataset developed for Named Entity Recognition in German federal court decisions. |
| Outcome: | The proposed dataset was developed for training an NER service for German legal documents in the EU project Lynx. |
Sensitive Data Detection and Classification in Spanish Clinical Text: Experiments with BERT (2020.lrec-1)
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| Challenge: | Massive digital data processing can endanger personal data privacy . anonymisation involves removing or replacing sensitive information from data . |
| Approach: | They propose to use a BERT-based sequence labelling model to conduct an experiment on clinical datasets in Spanish. |
| Outcome: | The proposed model outperforms existing models on clinical datasets in Spanish and shows that it is highly competitive with other models. |
Named Entities in Medical Case Reports: Corpus and Experiments (2020.lrec-1)
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| Challenge: | Only very few annotated corpora in the medical domain exist. |
| Approach: | They propose to annotate medical entities in case reports from PubMed Central's open access library. |
| Outcome: | The proposed corpus is the first of its kind to be made available to the scientific community in English. |
Hedwig: A Named Entity Linker (2020.lrec-1)
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| Challenge: | Named entity linking is the task of identifying mentions of named things in text . e.g., "Barack Obama" or "New York" are examples of named entities . |
| Approach: | They propose an end-to-end named entity linker that uses BILSTM models for mention detection and a PageRank algorithm for entity linking. |
| Outcome: | The proposed named entity linker performs better than the previous generation, and is trilingually better. |
An Experiment in Annotating Animal Species Names from ISTEX Resources (2020.lrec-1)
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| Challenge: | ISTEX platform provides access to 23 million scientific publications and is constantly enriched with information to improve the text quality to optimize machine processing. |
| Approach: | They propose to use two tools to detect animal species names in a corpus of 100 documents in zoology to improve their performance. |
| Outcome: | The proposed tools can be used to extract implicit knowledge and correlations from scientific publications and provide a reference corpus for use in TDM. |
Where are we in Named Entity Recognition from Speech? (2020.lrec-1)
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| Challenge: | Named entity recognition is usually made through a pipeline process that consists of processing audio and applying a NER to the audio outputs. |
| Approach: | They propose an original 3-pass approach and explore the capability of an E2E system to do structured NER. |
| Outcome: | The proposed system performs better than the current pipeline approach. |
Tagging Location Phrases in Text (2020.lrec-1)
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| Challenge: | a number of studies have focused on detecting named entities in written language. |
| Approach: | They describe a Location Phrase Detection task to detect non-named locations . they use sequential tagging and an annotation approach to create annotated datasets . |
| Outcome: | The proposed task can detect non-named locations in English and Russian news . the authors develop a sequential tagging approach and annotate datasets for English and Russia . |
ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition (2020.lrec-1)
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| Challenge: | Named entity recognition identifies common classes of noun phrases in text, but these entity labels are sparse, limiting utility to downstream tasks. |
| Approach: | They propose a name-based named entity recognition model that annotates all content words with a fine-grained semantic class label. |
| Outcome: | The proposed model achieves 0.85 F1 on the science exam domain domain . the proposed model is a powerful tool for question answering and inference . |
NorNE: Annotating Named Entities for Norwegian (2020.lrec-1)
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| Challenge: | Using the annotations of the existing treebank, we have created a dataset for named entity recognition for Norwegian. |
| Approach: | They propose to create a manually annotated corpus of named entities for Norwegian . they propose to add named entity annotations to existing treebank . |
| Outcome: | The proposed dataset extends the annotation of the existing Norwegian Dependency Treebank. |
Tag Me If You Can! Semantic Annotation of Biodiversity Metadata with the QEMP Corpus and the BiodivTagger (2020.lrec-1)
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| Challenge: | Dataset Retrieval is based on metadata, structured information about the primary data. |
| Approach: | They propose an ontology-based information extraction pipeline for biodiversity metadata that combines ontologies with semantic annotations to facilitate search. |
| Outcome: | The proposed pipeline is the first annotated metadata corpus for biodiversity research data. |
Towards a Versatile Medical-Annotation Guideline Feasible Without Heavy Medical Knowledge: Starting From Critical Lung Diseases (2020.lrec-1)
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| Challenge: | Current annotation policies for medical corpora are not standardized across clinical texts of different types. |
| Approach: | They propose to annotate medical records of various types using a named entity recognition (NER) task. |
| Outcome: | The proposed annotation scheme is applicable to large-scale clinical NLP projects. |
Creating a Dataset for Named Entity Recognition in the Archaeology Domain (2020.lrec-1)
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| Challenge: | Currently, there is no way to find 'by-catch', single finds of a different type, in the metadata of excavation reports. |
| Approach: | They propose to train NER classifiers on Dutch excavation reports to help archaeologists find structured information in archaic documents. |
| Outcome: | The proposed dataset contains 31k annotations between six entity types (artefact, time period, place, context, species & material). |
Development of a Medical Incident Report Corpus with Intention and Factuality Annotation (2020.lrec-1)
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| Challenge: | Medical incident reports are documents that record what happened in a medical incident. |
| Approach: | They propose to annotate medical incident reports with annotations of intention and factuality and medication entities and their relations. |
| Outcome: | The proposed method combines the definition of medication entities and the method to annotate the relations between entities and extracts important information from the unstructured part. |
ProGene - A Large-scale, High-Quality Protein-Gene Annotated Benchmark Corpus (2020.lrec-1)
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| Challenge: | Genes and proteins are fundamental entities of molecular genetics and are important for precision medicine. |
| Approach: | They propose to use a corpus of gene and protein names to cope with this class of named entities in a large-scale annotation campaign at the Jena University Language & Information Engineering lab. |
| Outcome: | The proposed corpus is an overall subdomain-independent corpus . it consists of 3,308 MEDLINE abstracts with over 36k sentences and more than 960k tokens annotated with nearly 60k named entity mentions. |
DaNE: A Named Entity Resource for Danish (2020.lrec-1)
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Rasmus Hvingelby, Amalie Brogaard Pauli, Maria Barrett, Christina Rosted, Lasse Malm Lidegaard, Anders Søgaard
| Challenge: | a named entity annotation for the Danish Universal Dependencies treebank is the largest publicly available named entity gold annotation. |
| Approach: | They propose a named entity annotation for the Danish Universal Dependencies treebank using the CoNLL-2003 annotation scheme DaNE. |
| Outcome: | The proposed annotations improve Danish named entity recognition over a recent cross-lingual approach and over norwegian training set. |
Fine-grained Named Entity Annotations for German Biographic Interviews (2020.lrec-1)
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| Challenge: | a NER annotation scheme is adapted for a corpus of transcripts of biographic interviews with emigrants to German . a dataset of spoken data and teaser tweets from newspaper sites are used to test the NER inventory. |
| Approach: | They propose a fine-grained NER annotation scheme with 30 labels and apply it to German data. |
| Outcome: | The proposed NER annotations can be applied to spoken data and teaser tweets from newspaper sites and achieve good inter-annotator agreement. |
A Broad-coverage Corpus for Finnish Named Entity Recognition (2020.lrec-1)
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| Challenge: | Named entity recognition (NER) is a fundamental task in natural language processing (NLP). |
| Approach: | They propose to annotate Finnish named entity names using a new corpus built on the Universal Dependencies corpus. |
| Outcome: | The new annotation identifies over 10,000 mentions and maintains compatibility with a previously released single-domain corpus for Finnish NER. |
Embeddings for Named Entity Recognition in Geoscience Portuguese Literature (2020.lrec-1)
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| Challenge: | Named Entity Recognition (NER) is a task within the field of Natural Language Processing that deals with the identification and categorization of Named entities (NEs) in a given text. |
| Approach: | They propose to use vector and tensor embeddings to train Portuguese Named Entity Recognition (NER) in the Geology domain. |
| Outcome: | The proposed model achieves state-of-the-art for the Portuguese Geology domain with one of its embeddings. |
Establishing a New State-of-the-Art for French Named Entity Recognition (2020.lrec-1)
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| Challenge: | Named entity recognition (NER) is a task consisting in identifying text spans that denote named entities such as person, location and organization names. |
| Approach: | They manually annotated the French TreeBank with information related to named entities . they sketch the underlying annotation guidelines and provide a few figures about the annotations . |
| Outcome: | The French TreeBank is the main source of morphosyntactic and syntactical annotations for French. |
Building OCR/NER Test Collections (2020.lrec-1)
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| Challenge: | Named entity recognition (NER) is the automatic recognition of spans of text as name mentions. |
| Approach: | They propose a method for annotating named entities on transcribed text . the transcriptions are all that is needed to evaluate the performance of OCR . |
| Outcome: | The proposed collection can be used to evaluate OCR and NER on transcribed text without re-annotation . the transcriptions are all that is needed to evaluate the performance of OCR . |
Reconstructing NER Corpora: a Case Study on Bulgarian (2020.lrec-1)
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| Challenge: | Named Entity Recognition (NER) and Named Enel Linking (NEL) are two related tasks that are under-resourced for the Slavic languages. |
| Approach: | They propose to use deep learning methods to improve a Named Entity Recognition corpus and to predict and annotate new types in a test corpus. |
| Outcome: | The proposed model improves a type-based Named Entity Recognition (NER) training corpus and predicts and annotates new types in a test corpus. |
MucLex: A German Lexicon for Surface Realisation (2020.lrec-1)
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| Challenge: | lexica for rule-based surface realisers for languages other than English are often scarce. |
| Approach: | They propose a German lexicon for the Natural Language Generation task of surface realisation based on the crowd-sourced online lexicont Wiktionary. |
| Outcome: | The proposed lexicon contains more than 100,000 lemmata and 670,000 different word forms. |
Generating Major Types of Chinese Classical Poetry in a Uniformed Framework (2020.lrec-1)
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| Challenge: | Chinese classical poetry is one of the most valuable literary and cultural heritages of China . it has many particular characteristics in its language structure, ranging from form, sound to meaning . a proposed uniformed framework for generating major types of Chinese classical poems is proposed . |
| Approach: | They propose a uniformed framework for generating major types of Chinese classical poems . they use a form- stressed weighting method to strengthen the control to the form of the generated poems a proposed framework is incorporated into Jiuge, the most influential Chinese classical poetry generation system developed by Tsinghua University. |
| Outcome: | The proposed framework can generate Chinese classical poems of major types with high quality in form and content. |
Video Caption Dataset for Describing Human Actions in Japanese (2020.lrec-1)
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| Challenge: | Existing video caption datasets for English have no equivalent for Japanese . authors evaluated two methods to obtain benchmark results . |
| Approach: | They propose to use Japanese video captions to describe human actions . they evaluated two different methods to obtain benchmark results . |
| Outcome: | The proposed dataset evaluates two different methods to obtain benchmark results . it shows that the generation methods can specify "who does what and where" |
Decode with Template: Content Preserving Sentiment Transfer (2020.lrec-1)
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| Challenge: | Existing methods to transfer sentiments for text use only explicit sentiments and templates to remove them from input sentences. |
| Approach: | They propose a method to transfer sentiments from input sentences to output sentences using templates. |
| Outcome: | The proposed model significantly outperforms state-of-the-art models in content preservation. |
Best Student Forcing: A Simple Training Mechanism in Adversarial Language Generation (2020.lrec-1)
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| Challenge: | Language models trained with Maximum Likelihood Estimation (MLE) have been considered as a mainstream solution in Natural Language Generation (NLG) however, they are reportedly suffering from training instability and mode collapse, and therefore outperform conventional MLE models. |
| Approach: | They propose a method to improve Generative Adversarial Nets (GANs) using best student forcing and discriminators to increase training stability and sample diversity. |
| Outcome: | The proposed techniques outperform MLE models and outperformed existing approaches in terms of sample diversity and training stability. |
Controllable Sentence Simplification (2020.lrec-1)
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| Challenge: | Text simplification is often considered an all-purpose generic task where the same simplifications are suitable for all but multiple audiences can benefit from simplified text in different ways. |
| Approach: | They propose a controllable simplification model that provides explicit control on simplification systems based on Sequence-to-Sequence models. |
| Outcome: | The proposed model outperforms standard models on simplification benchmarks. |
Exploring Transformer Text Generation for Medical Dataset Augmentation (2020.lrec-1)
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| Challenge: | Natural Language Processing (NLP) is a powerful tool to unlock the vast troves of unstructured data in clinical text. |
| Approach: | They propose a method for augmenting unstructured patient information to allow NLP model development on downstream clinically relevant tasks. |
| Outcome: | The proposed method beats baselines on a downstream classification task and can be used for NLP model development. |
Multi-lingual Mathematical Word Problem Generation using Long Short Term Memory Networks with Enhanced Input Features (2020.lrec-1)
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| Challenge: | Existing methods for multi-lingual MWP generation are incapable of identifying language specific constraints, especially in morphologically rich yet low resource languages such as Sinhala and Tamil. |
| Approach: | They propose to use a long-term memory network to generate elementary level MWPs by adding character embeddings, word embedds and Part of Speech (POS) tag embeddements to the network. |
| Outcome: | The proposed model generates elementary level MWPs while satisfying language specific constraints while providing attention for numerical values and units. |
Time-Aware Word Embeddings for Three Lebanese News Archives (2020.lrec-1)
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| Challenge: | a large corpus of newspaper archives has been generated, but historians have struggled to analyze it manually for decades. |
| Approach: | They propose to train word embeddings from three large Lebanese news archives, which collectively consist of 609,386 scanned newspaper images and span 151 years. |
| Outcome: | The embeddings are trained using a Google Tesseract 4.0 OCR engine and a benchmark of analogy tasks to evaluate their accuracy. |
GGP: Glossary Guided Post-processing for Word Embedding Learning (2020.lrec-1)
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| Challenge: | Existing word embedding models require much training time and domain knowledge to improve. |
| Approach: | They propose a GGP-based word embedding model that incorporates the glossary and learns sense representations. |
| Outcome: | The proposed model outperforms existing models on topical/functional similarity datasets by 4.1% and 7%. |
High Quality ELMo Embeddings for Seven Less-Resourced Languages (2020.lrec-1)
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| Challenge: | Recent results show that deep neural networks using contextual embeddings outperform non-contextual embedders on a majority of text classification tasks. |
| Approach: | They propose to use contextual embeddings for seven languages to train new embeddables . they also show that existing embeddibles for listed languages shall be improved . |
| Outcome: | The proposed embeddings outperform non-contextual embeddables on a majority of text classification tasks. |
Is Language Modeling Enough? Evaluating Effective Embedding Combinations (2020.lrec-1)
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Rudolf Schneider, Tom Oberhauser, Paul Grundmann, Felix Alexander Gers, Alexander Loeser, Steffen Staab
| Challenge: | specialized embeddings are not available for tasks like entity linking or paragraph classification. |
| Approach: | They evaluate whether universal embeddings can be complemented by specialized embeddables. |
| Outcome: | The proposed embeddings outperform state-of-the-art embeddables without any fine-tuning. |
Language Modeling with a General Second-Order RNN (2020.lrec-1)
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| Challenge: | a number of RNNs update their state as the input sequence is processed . second-order RNN architectures show promising performance in language modeling . |
| Approach: | They propose a second-order RNN architecture that generalizes existing ones . they use a Penn Treebank dataset to analyze how their different components affect performance . |
| Outcome: | The proposed architecture generalizes existing RNNs on a Penn Treebank dataset . it shows that removing the first-order terms does not hinder performance . |
Towards a Gold Standard for Evaluating Danish Word Embeddings (2020.lrec-1)
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| Challenge: | Existing word embedding models resemble semantic similarity solely by distribution, but there seems to be a need for future judgments to measure similarity in full context and along more than a single spectrum. |
| Approach: | They propose a model-agnostic similarity goal standard for evaluating Danish word embeddings based on human judgments made by 42 native speakers of Danish. |
| Outcome: | The goal standard is applied to evaluate Danish word embeddings on 42 native speakers of Danish. |
Urban Dictionary Embeddings for Slang NLP Applications (2020.lrec-1)
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| Challenge: | a new set of word embeddings is released to improve word embedment performance . word embeds provide useful representations of meanings of words in vectors . |
| Approach: | They present a set of word embeddings trained on Urban Dictionary . they show they have high performance across a range of common word embeding evaluations . |
| Outcome: | The first set of word embeddings trained on Urban Dictionary has high performance . the embeddables perform better on a range of common word evaluation tasks . |
Representation Learning for Unseen Words by Bridging Subwords to Semantic Networks (2020.lrec-1)
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| Challenge: | Pre-trained word embeddings only include words that appeared in corpora where pre-tried embedds are learned. |
| Approach: | They propose a method to represent out-of-vocabulary words using subword information and knowledge. |
| Outcome: | The proposed method improves performance over baselines that only use subwords or knowledge to represent OOV words. |
Give your Text Representation Models some Love: the Case for Basque (2020.lrec-1)
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Rodrigo Agerri, Iñaki San Vicente, Jon Ander Campos, Ander Barrena, Xabier Saralegi, Aitor Soroa, Eneko Agirre
| Challenge: | Word embeddings and pre-trained language models are expensive to train and are often used by small companies and research groups to build their own. |
| Approach: | They propose to use word embeddings and pre-trained language models to build rich representations of text and improve NLP tasks. |
| Outcome: | The proposed models perform better than publicly available versions in downstream NLP tasks for Basque. |
On the Correlation of Word Embedding Evaluation Metrics (2020.lrec-1)
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| Challenge: | Word embeddings are geometrical representations of word paradigmatics and syntagmatics. |
| Approach: | They propose to investigate evaluation metrics on various datasets to find correlations . they propose a fast solution to select the best word embeddings among many others . |
| Outcome: | The proposed method could be used to select the best word embeddings among many others. |
CBOW-tag: a Modified CBOW Algorithm for Generating Embedding Models from Annotated Corpora (2020.lrec-1)
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| Challenge: | Using word2vec, we train distributional semantic models that predict a word from the context or vice versa. |
| Approach: | They propose a modified version of the CBOW algorithm implemented in the fastText framework that includes the representation of original word forms and their annotation at the same time. |
| Outcome: | The proposed model can answer questions such as What do we eat?, What can we do with a skeleton?, etc. |
Much Ado About Nothing – Identification of Zero Copulas in Hungarian Using an NMT Model (2020.lrec-1)
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| Challenge: | Zero copulas are the phenomenon that nominal predicates lack an explicit verbal copule in default present tense 3rd person indicative cases. |
| Approach: | They propose a tool that can identify and mark the location of zero copulas in Hungarian clauses that contain nominal predicates at the right position. |
| Outcome: | The proposed tool can identify and mark the location of zero copulas, i.e. where an overt copulan would appear in the non-default cases. |
Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift (2020.lrec-1)
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| Challenge: | Existing methods for word embeddings have been used to model semantic relations with word embeds. |
| Approach: | They propose a method that leverages contextual embeddings for diachronic semantic shift detection by generating time specific word representations from BERT embedds. |
| Outcome: | The proposed method performs comparable to the current state-of-the-art without time consuming domain adaptation on large corpora. |
Improving NMT Quality Using Terminology Injection (2020.lrec-1)
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| Challenge: | a recent study has explored the use of vetted terminology in neural machine translation . a number of organizations use domain- or organization-specific words and phrases . |
| Approach: | They propose a method for injecting terminology and for evaluating terminology injection. |
| Outcome: | The proposed method is based on the long-term memory (LSTM) attention mechanism prevalent in state-of-the-art systems . it also introduces a new translation metric more sensitive to approved terminological content in MT output. |
Word Embedding Evaluation in Downstream Tasks and Semantic Analogies (2020.lrec-1)
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| Challenge: | Language Models (LMs) are an oft studied area of natural language processing . Word Embeddings (WE) are vector space representations of a vocabulary . |
| Approach: | They evaluate Word Embeddings (WE) models for the Portuguese langauage . results show that a diverse corpus can often outperform a larger, less textually diverse corp. |
| Outcome: | The proposed models outperform a larger, less textually diverse corpus in two tasks . the evaluation shows that a diverse and comprehensive corpus outperformed a smaller, less diverse corp. |
Detection of Reading Absorption in User-Generated Book Reviews: Resources Creation and Evaluation (2020.lrec-1)
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Piroska Lendvai, Sándor Darányi, Christian Geng, Moniek Kuijpers, Oier Lopez de Lacalle, Jean-Christophe Mensonides, Simone Rebora, Uwe Reichel
| Challenge: | a new study aims to detect how and when readers are experiencing engagement with a literary work . empirical literary studies and language technology are used to investigate reading absorption . |
| Approach: | They annotated user-generated book reviews with reading absorption categories . they then performed supervised binary classification of the mental state of absorption . |
| Outcome: | The proposed corpus of user-generated reviews is compared with machine learning models and a benchmark corpus. |
Developing an Arabic Infectious Disease Ontology to Include Non-Standard Terminology (2020.lrec-1)
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| Challenge: | Existing ontologies for Arabic are difficult due to the lack of resources and the nature of the Arabic language. |
| Approach: | They propose to build an Arabic ontology that integrates scientific vocabularies with informal equivalents. |
| Outcome: | The proposed ontology integrates scientific vocabularies with informal equivalents in Arabic . it will be automatically generated but the results will be evaluated by a domain expert . |
Aligning Wikipedia with WordNet:a Review and Evaluation of Different Techniques (2020.lrec-1)
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| Challenge: | a reliable alignment between WordNet and Wikipedia is a valuable resource for the creation of new wordnets in other languages and for the development of existing wordnet. |
| Approach: | They evaluate methods for aligning Wikipedia articles with WordNet synsets . they use a new gold and silver standard and a method that creates wordnets in other languages . |
| Outcome: | The proposed methods can be used to evaluate the quality of alignments between Wikipedia and WordNet synsets. |
The MWN.PT WordNet for Portuguese: Projection, Validation, Cross-lingual Alignment and Distribution (2020.lrec-1)
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António Branco, Sara Grilo, Márcia Bolrinha, Chakaveh Saedi, Ruben Branco, João Silva, Andreia Querido, Rita de Carvalho, Rosa Gaudio, Mariana Avelãs, Clara Pinto
| Challenge: | Lexical semantic networks are pervasive in natural language processing . Lexical ontologies play a key role in virtually all major applications . |
| Approach: | The present paper presents the MWN.PT WordNet for Portuguese . it is the largest high quality, manually validated and cross-lingually integrated wordnet of Portuguese based on the Princeton WordNet of English . |
| Outcome: | The MWN.PT WordNet for Portuguese includes 41,000 concepts expressed by 38,000 lexical units. |
Ontology-Style Relation Annotation: A Case Study (2020.lrec-1)
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| Challenge: | Existing methods for Relation Extraction (RE) annotations use links between entities . a domain link connects the relation mention to the source entity while a range link connect the relation to the destination entity. |
| Approach: | They propose an Ontology-Style Relation (OSR) annotation approach to find relation mentions in relation annotations. |
| Outcome: | The proposed approach can be easily converted to Ontology RDF triples to populate an Ontologies. |
The Ontology of Bulgarian Dialects – Architecture and Information Retrieval (2020.lrec-1)
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| Challenge: | The ontology of the Bulgarian Dialects demonstrates a novel usage of ontological modelling for the purposes of dialect digital archiving and information processing. |
| Approach: | They propose to use ontological modelling to capture the geographical distribution and diagnostic features of the Bulgarian dialects for digital archiving and information processing. |
| Outcome: | The ontology incorporates information on the dialects of the Bulgarian language and includes data from 84 dialects spoken not only on the territory of the Republic of Bulgaria, but also abroad. |
Spatial AMR: Expanded Spatial Annotation in the Context of a Grounded Minecraft Corpus (2020.lrec-1)
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| Challenge: | Existing annotation tools for spatial relations capture fine-grained semantics and pragmatics derived from spatial information. |
| Approach: | They propose an extension to the Abstract Meaning Representation annotation schema that captures fine-grained spatial information in grounded corpora. |
| Outcome: | The proposed tool can handle fine-grained spatial relationships grounded in quantized space. |
English WordNet Random Walk Pseudo-Corpora (2020.lrec-1)
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| Challenge: | a random walk over the WordNet taxonomy generates a set of pseudo-corpora . a resource description paper describes the creation and properties of such pseudo-corporates . |
| Approach: | They propose to use random walk to generate a set of pseudo-corpora over the English WordNet taxonomy. |
| Outcome: | The proposed pseudo-corpora can be used to train taxonomic word embeddings . the proposed pseudo corpora are generated from a random walk over the English wordnet taxonomy . |
On the Formal Standardization of Terminology Resources: The Case Study of TriMED (2020.lrec-1)
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| Challenge: | Terminology standardization plays an important role in the management of terminological resources. |
| Approach: | They propose to re-model an existing multilingual terminological database for the medical domain, TriMED, and propose a method to make it compliant to the latest ISO/TC 37 standards. |
| Outcome: | The proposed model should be compliant with the three most recent ISO/TC 37 standards and has a new data category repository and a Web application that can be used to access the multilingual terminological records. |
Metaphorical Expressions in Automatic Arabic Sentiment Analysis (2020.lrec-1)
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| Challenge: | Existing algorithms and tools for sentiment analysis are lacking in dealing with Arabic metaphorical expressions. |
| Approach: | They propose to use Arabic metaphors in automatic Arabic sentiment analysis to examine the performance of a state-of-art Arabic sentiment tool on metaphors. |
| Outcome: | The proposed model outperforms the state-of-the-art sentiment analysis tool on metaphors and gain a deeper insight into the issue. |
HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset (2020.lrec-1)
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| Challenge: | State-of-the-art deep learning-based recommender systems require large datasets to achieve their best performance. |
| Approach: | They propose to use TripAdvisor to build a large-scale hotel recommendation dataset with 50 million reviews. |
| Outcome: | The proposed dataset is the largest publicly available hotel recommendation dataset, based on TripAdvisor, with 50 million reviews. |
Doctor Who? Framing Through Names and Titles in German (2020.lrec-1)
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| Challenge: | Entity framing is the selection of aspects of an entity to promote a particular viewpoint towards that entity. |
| Approach: | They investigate entity framing of political figures through the use of names and titles in German online discourse. |
| Outcome: | The proposed method improves existing studies on German political discourse . it shows that the formality of naming correlates positively with stance in the tweets . |
Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification (2020.lrec-1)
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| Challenge: | Aspect-Target Sentiment Classification (ATSC) is a subtask of Aspect Based Sentimence Analysis (ABSA) . recent deep transfer-learning methods have been applied successfully to a myriad of NLP tasks. |
| Approach: | They propose to use a self-supervised domain-specific BERT language model to exploit ATSC . they also perform cross-domain evaluation to explore the real-world robustness of their models . |
| Outcome: | The proposed model outperforms baseline models on the SemEval 2014 task 4 restaurants dataset. |
An Empirical Examination of Online Restaurant Reviews (2020.lrec-1)
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| Challenge: | Existing methods for opinion mining and sentiment analysis focus on extracting either positive or negative opinions from texts and determining the targets of these opinions. |
| Approach: | They propose a corpus-based scheme that detects evaluative language at a finer-grained level. |
| Outcome: | The proposed scheme classifies each sentence into one of four evaluation types based on the proposed scheme. |
Manovaad: A Novel Approach to Event Oriented Corpus Creation Capturing Subjectivity and Focus (2020.lrec-1)
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| Challenge: | Several studies conducted on the different styles of reporting in journalism are essential in understanding phenomena such as media bias and multiple interpretations of the same event. |
| Approach: | They propose a novel method of event reporting that correlates the degree of subjectivity with the geographical closeness of reporting using a Bi-RNN model. |
| Outcome: | The proposed method correlates the degree of subjectivity with the geographical closeness of reporting using a Bi-RNN model. |
Toward Qualitative Evaluation of Embeddings for Arabic Sentiment Analysis (2020.lrec-1)
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| Challenge: | Existing studies on Arabic sentiment analysis (SA) tasks focus on word embeddings to capture semantic and syntactic similarities, but Arabic language is characterized by its agglutination and morphological richness contributing to great sparsity. |
| Approach: | They propose several protocols to evaluate specific embeddings for Arabic sentiment analysis task. |
| Outcome: | The proposed embeddings are based on words and lemmas in Arabic sentiment analysis (SA) task. |
Annotating Perspectives on Vaccination (2020.lrec-1)
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| Challenge: | Vaccination corpus is a corpus of texts related to the online vaccination debate . it contains documents from the Internet which reflect different views on vaccinations . |
| Approach: | They present a corpus of texts related to the online vaccination debate annotated with perspectives about attribution, claims and opinions. |
| Outcome: | The Vaccination Corpus contains 294 documents from the Internet which reflect different views on vaccinations. |
Aspect On: an Interactive Solution for Post-Editing the Aspect Extraction based on Online Learning (2020.lrec-1)
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| Challenge: | Experimental results show that Aspect On dramatically reduces the number of user clicks and effort required to post-edit the aspects extracted by the model. |
| Approach: | They propose an online learning-based aspect extraction solution that allows users to post-edit the aspect extraction with little effort. |
| Outcome: | The proposed solution dramatically reduces the number of user clicks and effort required to post-edit the aspects extracted by the model. |
Recommendation Chart of Domains for Cross-Domain Sentiment Analysis: Findings of A 20 Domain Study (2020.lrec-1)
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| Challenge: | Cross-domain sentiment analysis (CDSA) is a well-known problem in text analysis, but sufficient datasets may not be available for a domain to be trained. |
| Approach: | They propose to use 11 similarity metrics to facilitate cross-domain sentiment analysis to identify the best domains for CDSA for a given target domain. |
| Outcome: | The proposed approach performs better on 20 domain pairs and is validated by 11 similarity metrics. |
Inference Annotation of a Chinese Corpus for Opinion Mining (2020.lrec-1)
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| Challenge: | Existing tools for opinion mining can accurately predict the writer's attitude in simple explicit sentences. |
| Approach: | They propose to define inference, classify different types and provide an annotation framework to analyze the annotation results. |
| Outcome: | The proposed framework defines inference type, polarity and topic and analyzes the results. |
Cooking Up a Neural-based Model for Recipe Classification (2020.lrec-1)
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Elham Mohammadi, Nada Naji, Louis Marceau, Marc Queudot, Eric Charton, Leila Kosseim, Marie-Jean Meurs
| Challenge: | a dataset of cooking recipes in French is highly imbalanced due to collaborative nature of the dataset . authors propose a neural-based model to address the first task of the DEFT 2013 shared task . |
| Approach: | They propose a neural-based model to address the first task of the DEFT 2013 shared task . they use state-of-the-art embedding approaches and deep architectures to address imbalanced dataset . |
| Outcome: | The proposed model outperforms models that use only pretrained embeddings in micro and macro F1 scores. |
Enhancing a Lexicon of Polarity Shifters through the Supervised Classification of Shifting Directions (2020.lrec-1)
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| Challenge: | Existing polarity shifter lexica only specify when a word can cause shifting, but do not specify when this is limited to a single shifting direction. |
| Approach: | They propose a classifier that determines the shifting direction of polarity shifters by using resource-driven features and data-driven feature. |
| Outcome: | The proposed classifier enhances the largest available polarity shifter lexicon. |
Dataset Creation and Evaluation of Aspect Based Sentiment Analysis in Telugu, a Low Resource Language (2020.lrec-1)
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| Challenge: | Aspect Based Sentiment Analysis (ABSA) is a finer level sentiment analysis that assigns polarity to each targeted aspect instead of the entire review. |
| Approach: | They propose to use Telugu as a language for aspect based sentiment analysis . they use a resource that can be used to classify and categorise aspects of a review . |
| Outcome: | The proposed resource is based on a set of tasks in Telugu which demonstrate its reliability and usefulness. |
A Fine-grained Sentiment Dataset for Norwegian (2020.lrec-1)
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| Challenge: | Using a dataset for fine-grained sentiment analysis in Norwegian, we examine the annotation effort and provide an overview of the developed annotation guidelines. |
| Approach: | They propose a dataset for fine-grained sentiment analysis in Norwegian . they provide an overview of the developed annotation guidelines and analyze inter-annotator agreement . |
| Outcome: | The proposed dataset is the first of its kind for Norwegian and is available online. |
The Design and Construction of a Chinese Sarcasm Dataset (2020.lrec-1)
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| Challenge: | Existing sarcasm datasets are limited to English and Chinese . sarcasm is a multi-layered semi-conscious language phenomenon . |
| Approach: | They propose to build a high-quality Chinese sarcasm dataset using user comments . they use manual annotated sarkastic texts and non-sarcastic texts to train sarcasm classifier . |
| Outcome: | The proposed dataset contains 2,486 manual annotated sarcastic texts and 89,296 non-sarcatic texts. |
Target-based Sentiment Annotation in Chinese Financial News (2020.lrec-1)
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| Challenge: | Using a large corpus of 8,314 target-level sentiment annotations, sentiment classification on multiple opinion aspects/targets level is unsatisfactory. |
| Approach: | They propose to construct a large-scale target-based sentiment annotation corpus on Chinese financial news text. |
| Outcome: | The proposed corpus has 8,314 target-level sentiment annotations on Chinese financial news text. |
Multi-domain Tweet Corpora for Sentiment Analysis: Resource Creation and Evaluation (2020.lrec-1)
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| Challenge: | a huge amount of content is being generated every day due to the pervasiveness of social media. |
| Approach: | They firstly create a multi-domain tweet sentiment corpora and then establish a deep neural network based baseline framework to address the above mentioned issues. |
| Outcome: | The proposed dataset achieves 84.65% accuracy for sentiment analysis using a neural network, long short term memory, and gated recurrent unit (GRU). |
Reproduction and Revival of the Argument Reasoning Comprehension Task (2020.lrec-1)
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| Challenge: | Reproduction of scientific results is essential for scientific development across all disciplines. |
| Approach: | They evaluate scientific reproduction of arguments reasoning comprehension systems . they find reproducing results of previous work is a basic requirement for validating hypothesis . |
| Outcome: | The proposed systems were compared with the revised data set and scored in line with the results of the argument reasoning comprehension task. |
Design and Evaluation of SentiEcon: a fine-grained Economic/Financial Sentiment Lexicon from a Corpus of Business News (2020.lrec-1)
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| Challenge: | SentiEcon is a large, comprehensive, domain-specific computational lexicon designed for sentiment analysis applications. |
| Approach: | They describe a large, comprehensive, domain-specific computational lexicon designed for sentiment analysis applications. |
| Outcome: | The proposed lexicon significantly improves when adding sentiment words to the general-language sentiment lexiconic. |
ParlVote: A Corpus for Sentiment Analysis of Political Debates (2020.lrec-1)
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| Challenge: | Debate transcripts from the UK Parliament contain information about the positions taken by politicians towards important topics, but are difficult for humans to process manually. |
| Approach: | They propose to use a linear classifier and a transformer word embedding model to classify sentiment polarity in debate speeches to evaluate sentiment analysis systems for the political domain. |
| Outcome: | The proposed method performs better on the largest dataset and is more robust to other datasets. |
Offensive Language Detection Using Brown Clustering (2020.lrec-1)
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| Challenge: | a recent study shows that Brown clustering is of little use when distinguishing word polarity in sentiment analysis tasks. |
| Approach: | They investigate the use of Brown clustering for offensive language detection . they train Brown clusters separately on positive and negative sentiment data, then combine it into a single complex feature per word . |
| Outcome: | The proposed method improves offensive language detection when used as the only feature or with words or character n-grams. |
Annotating for Hate Speech: The MaNeCo Corpus and Some Input from Critical Discourse Analysis (2020.lrec-1)
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| Challenge: | Existing methods for detecting hate speech are based on the problem of identification, but there is no clear definition of hate speech. |
| Approach: | They propose a multi-layer annotation scheme for the detection of hate speech in a web 2.0 corpus . they propose to use a binary hate speech classification to identify hate speech . |
| Outcome: | The proposed scheme is piloted against a binary hate speech classification and appears to yield higher inter-annotator agreement. |
Marking Irony Activators in a Universal Dependencies Treebank: The Case of an Italian Twitter Corpus (2020.lrec-1)
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| Challenge: | Existing annotations for irony are difficult, and the recognition of it is difficult due to its polarity. |
| Approach: | They propose a fine-grained annotation scheme centered on irony that highlights the tokens responsible for its activation and their morpho-syntactic features. |
| Outcome: | The proposed scheme highlights the tokens responsible for irony activation and their morpho-syntactic features. |
HAHA 2019 Dataset: A Corpus for Humor Analysis in Spanish (2020.lrec-1)
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| Challenge: | 30,000 Spanish tweets were crowd-annotated with humor value and funniness score . the corpus contains approximately 38.6% of humorous tweets with an average score of 2.04 in a scale from 1 to 5 for the humorous tweet. |
| Approach: | They develop a corpus of 30,000 Spanish tweets crowd-annotated with humor value and funniness score. |
| Outcome: | The results obtained from the 30,000 tweets in the Spanish language are encouraging. |
Offensive Language Identification in Greek (2020.lrec-1)
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| Challenge: | a gap in the literature on offensive language has been addressed with studies on Spanish, Hindi, and German. |
| Approach: | They present a Greek annotated dataset for offensive language identification . it contains 4,779 tweets annotating offensive and not offensive posts from Twitter . they evaluate several computational models trained and tested on the dataset . |
| Outcome: | The proposed dataset contains 4,779 tweets annotated as offensive and not offensive . the authors show that the proposed dataset is similar to the OLID dataset for English . |
Syntax and Semantics in a Treebank for Esperanto (2020.lrec-1)
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| Challenge: | In this paper we describe syntactic and semantic aspects of Arbobanko, a treebank for the artificial language Esperanto, as well as tools and methods used in the production of the treebank. |
| Approach: | They describe syntactic and semantic aspects of Arbobanko, a treebank for the artificial language Esperanto, and evaluate tools and methods used in the production of the treebank. |
| Outcome: | The proposed treebank is enriched with lexical-semantic layer covering named entities, semantic type ontology for nouns and adjectives and framenet-inspired semantic classification of verbs. |
Implementation and Evaluation of an LFG-based Parser for Wolof (2020.lrec-1)
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| Challenge: | a parsing system for Wolof is developed based on the Lexical Functional Grammar (LFG) system provides detailed syntactic analysis essential for the further development of NLP applications. |
| Approach: | They propose a parsing system for Wolof based on the Lexical Functional Grammar (LFG) system uses finite-state transducers for word tokenization and morphological analysis . |
| Outcome: | The proposed system achieves 67.2% recall, 92.8% precision and an f-score of 77.9%. |
The Treebank of Vedic Sanskrit (2020.lrec-1)
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| Challenge: | Vedic Sanskrit is a morphologically rich ancient Indian language of central importance for linguistic and historical research. |
| Approach: | They introduce the first treebank of Vedic Sanskrit, a morphologically rich ancient Indian language . they describe how sentences are annotated in the Universal Dependencies scheme and which syntactic constructions required special attention. |
| Outcome: | The proposed treebank reflects the development of metrical and prose texts over a period of 600 years. |
Inherent Dependency Displacement Bias of Transition-Based Algorithms (2020.lrec-1)
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| Challenge: | Empirical studies have shown that performance varies across different treebanks in such a way that one algorithm outperforms another on one treebank and the reverse is true for a different tree bank. |
| Approach: | They introduce the concept of an algorithm’s inherent dependency displacement distribution and characterise its bias in terms of dependency displacement. |
| Outcome: | The proposed model shows that the similarity of an algorithm’s inherent dependency displacement distribution to a treebank’s displacement distribution is clearly correlated to the algorithm’ s parsing performance on that treebank. |
A Gold Standard Dependency Treebank for Turkish (2020.lrec-1)
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| Challenge: | Currently, Turkish treebanks are limited due to the limited number of annotated sentences in the domains of Wikipedia and ITU Web Treebanks. |
| Approach: | They propose to annotate Turkish web and Wikipedia sentences for segmentation, morphology, part-of-speech and dependency relations using tagsets and a Wikipedia section. |
| Outcome: | The proposed treebank is the largest publicly available morpho-syntactic treebank in terms of word count and has a dedicated Wikipedia section. |
Chunk Different Kind of Spoken Discourse: Challenges for Machine Learning (2020.lrec-1)
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| Challenge: | Existing chunkers for spoken data are based on a corpus composed of monologues and spontaneous talk in interaction. |
| Approach: | They propose to use CRFs to develop a chunker for spoken data . the chunker is based on a small corpus composed of two kinds of discourse . |
| Outcome: | The proposed chunker is based on a spoken corpus composed of monologue and spontaneous talk in interaction. |
GRAIN-S: Manually Annotated Syntax for German Interviews (2020.lrec-1)
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| Challenge: | GRAIN-S is a set of manually created syntactic annotations for radio interviews in germany. |
| Approach: | They propose to use GRAIN-S to create syntactic annotations for radio interviews in germany. |
| Outcome: | The proposed dataset extends an existing corpus GRAIN and comes with constituency and dependency trees for six interviews. |
Yorùbá Dependency Treebank (YTB) (2020.lrec-1)
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| Challenge: | Low-resource languages present enormous NLP opportunities as well as varying degrees of difficulties. |
| Approach: | They propose to use the Yoruba Bible treebank to apply a new grammar formalism to the language by examining the use of universal dependency annotations. |
| Outcome: | The treebank of hand-annotated parts of the Yoruba Bible provides an avenue for dependency analysis of the language; the application of a new grammar formalism to the language. |
English Recipe Flow Graph Corpus (2020.lrec-1)
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| Challenge: | Annotated corpus of English cooking recipe procedures with domain-specific linguistic and semantic structure. |
| Approach: | They annotate a corpus of English cooking recipe procedures with domain-specific linguistic and semantic structure and then use a flow graph to represent the sequence of steps. |
| Outcome: | The proposed methods achieve 71.1 to 87.5 F1 in the cooking domain and a flow graph achieves similarity to those used in Japanese recipes. |
Development of a General-Purpose Categorial Grammar Treebank (2020.lrec-1)
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| Challenge: | 'general-purpose' categorial grammar treebank is not tailored to specific variants of CG, but rather offers a theory-neutral linguistic resource that can be converted to different versions of 'type-logical grammar' . |
| Approach: | They propose a general-purpose categorial grammar treebank for Japanese that is not tailored to a specific variant of CG but rather offers a theory-neutral resource which can be converted to different versions of GC relatively easily. |
| Outcome: | The proposed treebank improves on the existing Japanese CG treebank on the treatment of certain linguistic phenomena (passives, causatives, and control/raising predicates). |
Dependency Parsing for Urdu: Resources, Conversions and Learning (2020.lrec-1)
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| Challenge: | Existing treebanks for Urdu are under-resourced due to lack of resources. |
| Approach: | They propose to convert existing treebanks into a common format that is based on Universal Dependencies. |
| Outcome: | The proposed format outperforms the MaltParser and a transition-based BiLSTM parser with word embeddings and significantly improves parsing accuracy. |
Prague Dependency Treebank - Consolidated 1.0 (2020.lrec-1)
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Jan Hajič, Eduard Bejček, Jaroslava Hlavacova, Marie Mikulová, Milan Straka, Jan Štěpánek, Barbora Štěpánková
| Challenge: | Using the standard PDT scheme, the Prague Dependency Treebank-Consolidated 1.0 contains 4 different datasets of Czech, uniformly annotated using the standard scheme. |
| Approach: | They present a richly annotated and genre-diversified language resource, the Prague Dependency Treebank-Consolidated 1.0, which contains 4 different datasets of Czech, uniformly annnotated using the standard PDT scheme. |
| Outcome: | The Prague Dependency Treebank-Consolidated 1.0 contains 4 datasets of Czech, uniformly annotated using the standard PDT scheme. |
Training a Swedish Constituency Parser on Six Incompatible Treebanks (2020.lrec-1)
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| Challenge: | Syntactic parsing is a widely used intermediate step in several natural language processing tasks. |
| Approach: | They propose to use a function-tagged constituent treebank for Swedish which includes discontinuous constituents to improve the accuracy. |
| Outcome: | The proposed parser can be trained on additional treebanks that use other annotation models. |
Parsing as Tagging (2020.lrec-1)
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| Challenge: | Existing methods for dependency parsing treat parse as tagging, but they are not perfect. |
| Approach: | They propose a simple yet accurate method that treats parsing as tagging . they use a sequence model with a bidirectional LSTM over BERT embeddings . |
| Outcome: | The proposed method outperforms the state-of-the-art method on universal dependency (UD) by 1.76% unlabeled attachment score (UAS) for English, 1.98% UAS for French, and 1.16% UAS in German. |
The EDGeS Diachronic Bible Corpus (2020.lrec-1)
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| Challenge: | EDGeS is a diachronic and parallel corpus of Bible translations in Dutch, English, German and Swedish . it is intended to be used for longitudinal studies of complex verb constructions in Germanic . |
| Approach: | They present the EDGeS Diachronic Bible Corpus, a diachronic corpus of Bible translations in Dutch, English, German and Swedish . they use a synchronically and synchronly parallel corpus to study complex verb constructions in Germanic . |
| Outcome: | The EDGeS is a diachronic and parallel corpus of Bible translations in Dutch, English, German and Swedish spanning six and a half centuries. |
Treebanking User-Generated Content: A Proposal for a Unified Representation in Universal Dependencies (2020.lrec-1)
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Manuela Sanguinetti, Cristina Bosco, Lauren Cassidy, Özlem Çetinoğlu, Alessandra Teresa Cignarella, Teresa Lynn, Ines Rehbein, Josef Ruppenhofer, Djamé Seddah, Amir Zeldes
| Challenge: | Despite the increasing number of contributions on Part-of-Speech tagging and parsing, automatic processing of user-generated content (UGC) still represents a challenging task. |
| Approach: | They propose a set of guidelines for the annotation of user-generated texts within the Universal Dependencies framework. |
| Outcome: | The proposed annotation guidelines promote cross-linguistic consistency, which has always been in the spirit of UD. |
A Diachronic Treebank of Russian Spanning More Than a Thousand Years (2020.lrec-1)
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| Challenge: | TOROT is a treebank that spans from the earliest Old Church Slavonic to modern Russian texts. |
| Approach: | They describe a new version of the Troms Old Russian and Old Church Slavonic Treebank . it adds a modern subcorpus to the existing treebank of contemporary standard Russian . they describe the conversion of SynTagRus into a treebank covering every attested stage of Russian and OCS . |
| Outcome: | The TOROT 20200116 treebank covers all attested stages of Russian and OCS . it includes a modern subcorpus that was created by a conversion of the SynTagRus treebank . |
ÆTHEL: Automatically Extracted Typelogical Derivations for Dutch (2020.lrec-1)
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| Challenge: | THEL contains a lexicon of supertags for about 900 000 words in context. |
| Approach: | They propose to extract a lexicon of supertags for 900 000 words in context and provide 72 192 validated derivations on the basis of these types. |
| Outcome: | The proposed dataset contains lexicon of supertags for about 900 000 words in context. |
AMALGUM – A Free, Balanced, Multilayer English Web Corpus (2020.lrec-1)
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| Challenge: | a corpus of 4M tokens is available online with a large number of high-quality annotation layers. |
| Approach: | They propose to use a genre-balanced English web corpus with multiple annotation layers . they harness knowledge from multiple annotation layer to achieve a "better than NLP" benchmark . |
| Outcome: | The proposed corpus is genre-balanced and features high-quality automatic annotation layers. |
Typical Sentences as a Resource for Valence (2020.lrec-1)
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| Challenge: | Verb valence information can be derived from corpora by using subcorpora of typical sentences that are constructed in a language independent manner based on frequent POS structures. |
| Approach: | They propose a method which allows one to 'strip' the POS-pattern of a sentence with various types of adjuncts down to those items representing the valence frame instantiated by the verb in question. |
| Outcome: | The proposed approach can be used to construct valence pairs for a bilingual polyval dictionary using the Norwegian valance dictionary NorVal. |
Recognizing Sentence-level Logical Document Structures with the Help of Context-free Grammars (2020.lrec-1)
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| Challenge: | Current sentence boundary detectors split documents into sequentially ordered sentences without their dependencies. |
| Approach: | They propose a tool that segments sentences into tree structures to detect recursive structure . they retrain different constituency parsers to transform them into sentence segmenters . |
| Outcome: | The proposed tool can detect recursive structure in documents with a main clause and subordinate clauses . the proposed tool improves German dependency parsing by providing additional structural information. |
When Collaborative Treebank Curation Meets Graph Grammars (2020.lrec-1)
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| Challenge: | Arborator-Grew is a collaborative annotation tool for treebank development. |
| Approach: | They present a collaborative annotation tool for treebank development that combines the features of Arborator and Grew. |
| Outcome: | The proposed tool is a complete redevelopment and modernization of Arborator, replacing its internal database storage by a new Grew API. |
ODIL_Syntax: a Free Spontaneous Spoken French Treebank Annotated with Constituent Trees (2020.lrec-1)
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| Challenge: | ODIL Syntax is a French treebank built on spontaneous speech transcripts . the structure of every speech turn is represented by constituent trees . |
| Approach: | They propose a French treebank built on spontaneous speech transcripts with a constituency tree representation. |
| Outcome: | The proposed treebank is based on the French TreeBank, with some annotation guidelines . the proposed tree bank will be freely distributed by January 2020 under a Creative Commons licence . |
Towards the Conversion of National Corpus of Polish to Universal Dependencies (2020.lrec-1)
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| Challenge: | a paper aims at enriching the manually annotated part of National Corpus of Polish with a syntactic layer. |
| Approach: | They enrich manually annotated part of Polish National Corpus with a syntactic layer and a UD dependency graph. |
| Outcome: | The proposed model outperforms a model trained on a smaller set of gold-standard trees in predicting part-of-speech tags, morphological features, lemmata and labelled dependency trees. |
SegBo: A Database of Borrowed Sounds in the World’s Languages (2020.lrec-1)
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| Challenge: | phonological segment borrowing is a process through which languages acquire new contrastive speech sounds as the result of borrowing words from other languages. |
| Approach: | They propose to use a database to aggregate borrowed phonological segments from languages to create a new contrastive sound. |
| Outcome: | The proposed database is based on a cross-linguistic database of borrowed phonological segments. |
Developing Resources for Automated Speech Processing of Quebec French (2020.lrec-1)
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| Challenge: | acoustic models for automatic segmentation of Quebec French are not available for all languages . linguistic resources are developed to perform phonetic annotations in Quebec French . physical characteristics of speech can be observed in the production of sounds . |
| Approach: | They propose to use a French lexicon to train automatic QF segmentation models . they adapt existing pronunciation dictionary and acoustic model from existing ones . |
| Outcome: | The proposed tools perform the full process of speech segmentation in Quebec French. |
AlloVera: A Multilingual Allophone Database (2020.lrec-1)
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David R. Mortensen, Xinjian Li, Patrick Littell, Alexis Michaud, Shruti Rijhwani, Antonios Anastasopoulos, Alan W Black, Florian Metze, Graham Neubig
| Challenge: | Phonemes are contrastive phonological units, and allophones are their various concrete realizations. |
| Approach: | They propose a resource that maps allophones to phonemes for 14 languages . they propose phonological representations that are much closer to a universal transcription . |
| Outcome: | The proposed resource maps from 218 allophones to phonemes for 14 languages. |
Arabic Speech Rhythm Corpus: Read and Spontaneous Speaking Styles (2020.lrec-1)
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| Challenge: | a corpus of Arabic speech recordings has been built to allow comparisons between Arabic and other languages. |
| Approach: | They propose to build a corpus of Arabic speech recordings that can be compared with other languages. |
| Outcome: | The proposed corpus can be used for forensic phonetic research and casework applications. |
Comparing Methods for Measuring Dialect Similarity in Norwegian (2020.lrec-1)
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| Challenge: | a coarse-grained transcription of speech is sufficient to replicate dialectal boundaries, but it can be generalised over by an automatic method. |
| Approach: | They propose to use two different methods to measure dialect similarity in Norwegian . they use the Levenshtein method and the neural long short term memory algorithm . the paper shows that coarse-grained transcriptions of speech can generate dialect maps . |
| Outcome: | The proposed method can generalise over coarse-grained transcriptions, but it needs a large dataset . the proposed method is compared with canonical maps found in the dialect literature . |
AccentDB: A Database of Non-Native English Accents to Assist Neural Speech Recognition (2020.lrec-1)
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| Challenge: | aaron e. sanchez and joe saunders: automatic speech recognition still faces a major challenge . they say accents are a way of pronouncing a language, and speakers always have manner of speaking . esassen: accents can be used to identify non-native speakers of a speech . |
| Approach: | They propose to create a database of speech samples in non-native accents for ASR testing . they also propose to introduce accent neutralization of non- native accents to native accent . |
| Outcome: | The proposed model is compared against human-labelled accent classes and is generalized against human data. |
A Framework for Evaluation of Machine Reading Comprehension Gold Standards (2020.lrec-1)
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| Challenge: | Existing literature on machine reading comprehension (MRC) data is limited on the data design of gold standards. |
| Approach: | They propose a framework to investigate linguistic features, lexical cues and ambiguity in MRC gold standards. |
| Outcome: | The proposed framework investigates the present linguistic features, required reasoning and background knowledge and factual correctness on the one hand, and the presence of lexical cues as a lower bound for the requirement of understanding on the other. |
Multi-class Hierarchical Question Classification for Multiple Choice Science Exams (2020.lrec-1)
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Dongfang Xu, Peter Jansen, Jaycie Martin, Zhengnan Xie, Vikas Yadav, Harish Tayyar Madabushi, Oyvind Tafjord, Peter Clark
| Challenge: | Prior work has demonstrated that question classification (QC) can help answer a question more accurately. |
| Approach: | They propose to use a large dataset for question classification (QC) that contains 7,787 science exam questions paired with detailed classification labels from a fine-grained hierarchical taxonomy of 406 problem domains to train a BERT-based model. |
| Outcome: | The proposed model achieves a large (+0.12 MAP) gain while also achieving state-of-the-art performance on benchmark open-domain and biomedical QC datasets. |
Assessing Users’ Reputation from Syntactic and Semantic Information in Community Question Answering (2020.lrec-1)
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| Challenge: | Existing reputation systems do not take linguistic quality into account in reputation scores estimation. |
| Approach: | They build statistical models that learn reputation from syntactic and semantic structures extracted from their associated answers content. |
| Outcome: | The proposed models show that users’ writing styles play important roles in building reputation points. |
Unsupervised Domain Adaptation of Language Models for Reading Comprehension (2020.lrec-1)
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| Challenge: | State-of-the-art reading comprehension models do not have general linguistic intelligence . accuracy of out-domain datasets is affected by the distribution of data . |
| Approach: | They propose to use supervised RC training data in the source domain and unlabeled passages in the target domain to adapt models. |
| Outcome: | The proposed model outperforms the model without domain adaptation with five datasets in different domains. |
Propagate-Selector: Detecting Supporting Sentences for Question Answering via Graph Neural Networks (2020.lrec-1)
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| Challenge: | Existing question-answering models do not require reasoning across sentences in the given context (passage). |
| Approach: | They propose a graph neural network that propagates information over sentences to understand information that cannot be inferred when considering sentences in isolation. |
| Outcome: | The proposed approach obtains the best performance compared to the widely used answer-selection models that do not consider the intersentential relationship. |
An Empirical Comparison of Question Classification Methods for Question Answering Systems (2020.lrec-1)
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| Challenge: | Existing methods for Question Classification are monolingual, but they are not suitable for low-resourced languages. |
| Approach: | They propose to classify the most recent methods in four different categories . they propose to use a low, medium, high, and very high level of dependency on external resources . |
| Outcome: | The proposed method outperforms methods not suitable for low-resource languages. |
Cross-sentence Pre-trained Model for Interactive QA matching (2020.lrec-1)
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| Challenge: | Existing methods for semantic matching do not examine each sentence individually, but consider syntactic context inside a sentence. |
| Approach: | They propose a semantic matching model that takes a cross-sentence context-aware architecture and incorporates a quantity of context information jump to facilitate attention weight formulation. |
| Outcome: | The proposed model outperforms state-of-the-art models on the Yahoo! community question dataset and the TREC library. |
SQuAD2-CR: Semi-supervised Annotation for Cause and Rationales for Unanswerability in SQuAD 2.0 (2020.lrec-1)
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| Challenge: | Existing models are brittle for adversarial perturbed questions, causing uncertainty . a dataset with annotations on unanswerable questions is not available to solve this problem . |
| Approach: | They use crowdsourced annotations to annotate unanswerable questions . they also annotated which part of the question causes unanswered questions a . |
| Outcome: | The proposed dataset can be used to improve model interpretation, authors say . they find that existing models are brittle for adversarial perturbed questions . |
Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs (2020.lrec-1)
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Takashi Kodama, Ryuichiro Higashinaka, Koh Mitsuda, Ryo Masumura, Yushi Aono, Ryuta Nakamura, Noritake Adachi, Hidetoshi Kawabata
| Challenge: | Existing approaches to realize consistent personalities require expensive data collection. |
| Approach: | They propose to collect question-answer pairs for particular characters from online users . meta information such as emotion and intimacy was also collected . |
| Outcome: | The proposed method can be used to train neural conversational models with high quality questions and meta information. |
AIA-BDE: A Corpus of FAQs in Portuguese and their Variations (2020.lrec-1)
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Hugo Gonçalo Oliveira, João Ferreira, José Santos, Pedro Fialho, Ricardo Rodrigues, Luisa Coheur, Ana Alves
| Challenge: | a corpus of 380 domain-oriented FAQs in Portuguese is presented . paraphrases or entailed questions are created manually, by humans, or automatically, with Google Translate. |
| Approach: | They present a corpus of 380 domain-oriented FAQs in Portuguese and their variations, i.e., paraphrases or entailed questions, created manually, by humans, or automatically, with Google Translate. |
| Outcome: | The proposed system outperforms other systems in the domain of question-answering . it performs well when matching variations with their original questions . |
TutorialVQA: Question Answering Dataset for Tutorial Videos (2020.lrec-1)
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| Challenge: | a new question answering task on instructional videos is needed due to their verbose nature . factoid questions are only a small part of what people actually want to ask on video contents . |
| Approach: | They propose a question answering task on instructional videos based on video transcripts . they use a dataset consisting of 6,000 manually collected triples of (video, question, answer span) |
| Outcome: | The proposed task focuses on screencast tutorial videos pertaining to an image editing program. |
WorldTree V2: A Corpus of Science-Domain Structured Explanations and Inference Patterns supporting Multi-Hop Inference (2020.lrec-1)
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Zhengnan Xie, Sebastian Thiem, Jaycie Martin, Elizabeth Wainwright, Steven Marmorstein, Peter Jansen
| Challenge: | Standardized science questions require combining an average of 6 facts and as many as 16 facts to answer and explain. |
| Approach: | They propose to combine an average of 6 facts and as many as 16 facts to produce an answer for complex questions. |
| Outcome: | The proposed model is based on a corpus of 5,114 standardized science exam questions . it uses multi-fact explanations that combine science knowledge and world knowledge . |
Chat or Learn: a Data-Driven Robust Question-Answering System (2020.lrec-1)
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| Challenge: | QA systems tend to perform poorly at chitchat, while data-driven chatbots are typically user-friendly but not goal-oriented . |
| Approach: | They propose to use a controller to perform dialogue act classification and feed user input either to a sequence-to-sequence chatbot or to QA systems. |
| Outcome: | The proposed system is a spoken QA application for the Google Home smart speaker. |
Project PIAF: Building a Native French Question-Answering Dataset (2020.lrec-1)
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Rachel Keraron, Guillaume Lancrenon, Mathilde Bras, Frédéric Allary, Gilles Moyse, Thomas Scialom, Edmundo-Pavel Soriano-Morales, Jacopo Staiano
| Challenge: | a lack of data for non-English languages is limiting the development of downstream tasks such as Question Answering. |
| Approach: | They propose to collect a native French Question Answering Dataset using a participatory setup. |
| Outcome: | The proposed tool allows volunteers to participate in crowdsourced annotations in French. |
Cross-lingual and Cross-domain Evaluation of Machine Reading Comprehension with Squad and CALOR-Quest Corpora (2020.lrec-1)
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| Challenge: | a recent study has shown that language mismatch and domain mismatch can affect performance of a machine reading task . a factor between language mismatched and domain-mismatched has the strongest influence on performance . |
| Approach: | They compare the cross-language and cross-domain capabilities of BERT on a machine reading comprehension task on two corpora: SQuAD and a new French Machine Reading dataset. |
| Outcome: | The proposed model matches human performance on a machine reading comprehension task with BERT on Chinese and French documents with interesting results. |
ScholarlyRead: A New Dataset for Scientific Article Reading Comprehension (2020.lrec-1)
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| Challenge: | Existing studies on MRC on scholarly articles have focused on general domain datasets of news articles and elementary school-level storybooks. |
| Approach: | They propose to generate automatic questions from span-of-word-based scholarly articles’ Reading Comprehension dataset with approximately 10K manually checked passage-question-answer instances. |
| Outcome: | The proposed model yields the F1 score of 37.31% and is useful for building Question-Answering (QA) systems on scientific articles. |
Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection Task (2020.lrec-1)
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| Challenge: | Word embeddings that consider context have attracted great attention for natural language processing tasks in recent years. |
| Approach: | They propose two different approaches to integrate contextualized word embeddings with transformer encoders for sentence similarity modeling. |
| Outcome: | The proposed model outperforms the feature-based approach on six datasets. |
Automatic Spanish Translation of SQuAD Dataset for Multi-lingual Question Answering (2020.lrec-1)
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| Challenge: | Existing methods to train multilingual QA systems are limited for other languages . cross-lingual learning is a technique that transfers knowledge from source to target language with fewer training data. |
| Approach: | They propose a translation method to translate the Stanford Question Answering Dataset to Spanish and a multilingual-BERT model to train Spanish QA systems. |
| Outcome: | The proposed method outperforms the previous benchmarks for cross-lingual extractive QA. |
A Corpus for Visual Question Answering Annotated with Frame Semantic Information (2020.lrec-1)
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| Challenge: | Visual Question Answering (VQA) is a computer vision problem. |
| Approach: | They propose to annotate a visual question answering dataset with verb semantics to help the model understand verbs. |
| Outcome: | The proposed system is built on the imSitu dataset annotated with verb semantic information. |
Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering (2020.lrec-1)
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| Challenge: | Existing work on EHR QA models that learn to answer questions from structured data has focused on analyzing questions or mapping questions to existing NLP based information extraction models. |
| Approach: | They conduct 48 experiments on two clinical question answering datasets . they use open-domain and domain-specific corpora to fine-tune Transformer language models . |
| Outcome: | The proposed models can learn to answer questions from unstructured notes with accuracies up to 90% on open-domain and domain-specific corpora. |
A Shared Task of a New, Collaborative Type to Foster Reproducibility: A First Exercise in the Area of Language Science and Technology with REPROLANG2020 (2020.lrec-1)
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António Branco, Nicoletta Calzolari, Piek Vossen, Gertjan Van Noord, Dieter van Uytvanck, João Silva, Luís Gomes, André Moreira, Willem Elbers
| Challenge: | Scientific knowledge is grounded on falsifiable predictions and therefore its credibility and raison d'être rely on the possibility of repeating experiments and getting similar results as originally obtained and reported. |
| Approach: | They propose a collaborative task which is collaborative rather than competitive and supports reproduction of research results. |
| Outcome: | The proposed task is called REPROLANG-The Shared Task on the Reproduction of Research Results in Science and Technology of Natural Language Processing (LREC2020). |
A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well (2020.lrec-1)
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| Challenge: | Existing methods for fully unsupervised cross-lingual mapping of word embeddings are available to achieve such a mapping . |
| Approach: | They reproduce the experiments of Artetxe and Sgaard (2018) . they propose a robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. |
| Outcome: | The proposed method is feasible with minor assumptions, and it is able to be replicated in four languages. |
A Closer Look on Unsupervised Cross-lingual Word Embeddings Mapping (2020.lrec-1)
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| Challenge: | Existing methods for word embeddings are limited to a single, unannotated corpus, which means that word representations with similar meaning in distinct languages can be very different. |
| Approach: | They propose an unsupervised method for cross-lingual word embedding mapping that uses stochastic initialization and isometric initialization to verify the method's robustness. |
| Outcome: | The proposed method is robust on different embedding representations and new language pairs, particularly those involving Slavic languages like Polish or Czech. |
Reproducing a Morphosyntactic Tagger with a Meta-BiLSTM Model over Context Sensitive Token Encodings (2020.lrec-1)
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| Challenge: | Reproducibility of research results is only recently beginning to be practiced and acknowledged . a research culture that focuses on beating previous benchmarks while disregarding the need to contribute to scientific knowledge and understanding is a problem, says a researcher. |
| Approach: | They reproduced work on morphosyntactic tagging using a meta-model . they did not contact the original authors for reproduction . |
| Outcome: | The proposed model outperforms previous models on morphological tagging tasks but fails to match the F1-scores reported for the meta-BiLSTM model. |
Reproducing Neural Ensemble Classifier for Semantic Relation Extraction inScientific Papers (2020.lrec-1)
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| Challenge: | Replicability and reproducibility are core ideas of modern scientific methods. |
| Approach: | They describe challenges encountered in reproducing the results of a top performing system in computational linguistics. |
| Outcome: | The proposed system was able to reproduce the results of a task 7 in the domain of natural language processing and computational linguistics. |
ULMFiT replication (2020.lrec-1)
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| Challenge: | Inductive transfer learning has been well studied in Computer Vision and in Natural Language Understanding/Processing. |
| Approach: | They propose to use the knowledge gained by solving a source problem towards solving another (target) problem T t. |
| Outcome: | The problem of English text classification is motivated by practical applications like anomaly detection, security and legal applications. |
CombiNMT: An Exploration into Neural Text Simplification Models (2020.lrec-1)
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| Challenge: | Neural Text Simplification (NMT) is a widely used technique in Machine Translation (NLP) |
| Approach: | They present a replication study of Exploring Neural Text Simplification Models using OpenNMT and Newsela datasets. |
| Outcome: | The proposed systems improve on the original paper by using an updated implementation of OpenNMT and the newsela corpus alongside the original Wikipedia dataset. |
Reproducing Monolingual, Multilingual and Cross-Lingual CEFR Predictions (2020.lrec-1)
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| Challenge: | POStag and dependency n-grams are more effective than text length and global linguistic indices for this kind of task. |
| Approach: | They propose to use POStag and dependency n-grams to predict the quality of a text written by learners of another language to categorize texts according to their CEFR level. |
| Outcome: | The proposed model is more effective than POStag and dependency n-grams in cross-lingual experiments than the previous models. |
Reproduction and Replication: A Case Study with Automatic Essay Scoring (2020.lrec-1)
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| Challenge: | reproducibility of experiments has gained more attention in the NLP community . recent negative reproduction results indicate that published results are not verifiable . |
| Approach: | They propose to reproduce an earlier study of automatic essay scoring for determining the proficiency of second language learners in a multilingual setting. |
| Outcome: | The proposed reproduction of an AES system for determining the proficiency of second language learners in a multilingual setting is compared with the original. |
REPROLANG 2020: Automatic Proficiency Scoring of Czech, English, German, Italian, and Spanish Learner Essays (2020.lrec-1)
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| Challenge: | a new paper aims to reproduce the work described in Vajjala & Rama (2018) . the paper focuses on features-based and neural approaches to essay scoring in Czech, German and Italian . |
| Approach: | They propose to replicate the work described in Vajjala & Rama 2018, ‘Experiments with universal CEFR classification’, as part of REPROLANG 2020. |
| Outcome: | The proposed methods perform better than feature-based models for large text datasets, though neural network modifications do bring performance closer to the best feature-driven models. |
Language Proficiency Scoring (2020.lrec-1)
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| Challenge: | a new paper evaluates and extends the results of an automated proficiency classification system for different languages. |
| Approach: | They propose to extend an automated essay scoring system proposed by CEFR . they compare results with those from previous paper and add a new corpus for english . |
| Outcome: | The proposed approach does not scale well with the added English corpus. |
The Learnability of the Annotated Input in NMT Replicating (Vanmassenhove and Way, 2018) with OpenNMT (2020.lrec-1)
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| Challenge: | reproducibility of experiments is a key issue in Neural Networks, which are fed with variable samples of training data. |
| Approach: | They reproduce some of the experiments related to neural network training for Machine Translation as reported in . they annotated a sample from the EN-FR and EN-DE Europarl with syntactic and semantic annotations to train neural networks with the Nematus Neural Machine Translation toolkit. |
| Outcome: | The results obtained were lower than the original paper, but on a more limited set of annotations. |
KGvec2go – Knowledge Graph Embeddings as a Service (2020.lrec-1)
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| Challenge: | Currently, we serve pre-trained graph embeddings for four knowledge graphs . KGvec2go is a Web API for accessing and consuming graph embeds based on a knowledge graph . |
| Approach: | They propose a Web API for accessing and consuming graph embeddings in a light-weight fashion in downstream applications. |
| Outcome: | The proposed service can be used in machine learning, document modeling, and recommender systems. |
Ontology Matching Using Convolutional Neural Networks (2020.lrec-1)
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| Challenge: | a growing number of ontologies require effective ways to align different ontology models . traditional methods to align ontological models are based on string metrics and structure analysis . but convolutional neural networks can be applied as-is to any domain, allowing for cross-domain applications . |
| Approach: | They propose a method to align ontologies automatically using machine learning techniques . they use convolutional neural networks to perform string matching between class labels . |
| Outcome: | The proposed method achieves state-of-the-art on ontologies from the Ontology Alignment Evaluation Initiative (OAEI) it maintains good performance when tested on a different domain, which could lead to cross-domain applications. |
Defying Wikidata: Validation of Terminological Relations in the Web of Data (2020.lrec-1)
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| Challenge: | a new approach to validate terminological data retrieved from open encyclopaedic knowledge bases is needed . the legal domain is one of the most valuable areas of knowledge in the world . |
| Approach: | They propose to validate terminological data retrieved from open encyclopaedic knowledge bases by enriching them with information from existing resources in the Semantic Web. |
| Outcome: | The proposed method validates terms from open encyclopaedic knowledge bases in four languages. |
Recent Developments for the Linguistic Linked Open Data Infrastructure (2020.lrec-1)
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Thierry Declerck, John Philip McCrae, Matthias Hartung, Jorge Gracia, Christian Chiarcos, Elena Montiel-Ponsoda, Philipp Cimiano, Artem Revenko, Roser Saurí, Deirdre Lee, Stefania Racioppa, Jamal Abdul Nasir, Matthias Orlikowsk, Marta Lanau-Coronas, Christian Fäth, Mariano Rico, Mohammad Fazleh Elahi, Maria Khvalchik, Meritxell Gonzalez, Katharine Cooney
| Challenge: | Language data is rarely 'ready-to-use' and language technology specialists spend over 80% of their time cleaning, organizing and collecting language datasets. |
| Approach: | They propose a methodology for building data value chains based around language resources and language technologies that can be integrated by means of semantic technologies. |
| Outcome: | The proposed methodology is based on language resources and language technologies that can be integrated by means of semantic technologies. |
Annotation Interoperability for the Post-ISOCat Era (2020.lrec-1)
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| Challenge: | Using ISOCat successor solutions, annotation standards have been developed since 2010 . |
| Approach: | They describe ISOCat successor solutions and annotation standardization efforts since 2010 . they describe low-cost harmonization of post-ISOCat vocabularies by means of linked ontologies . |
| Outcome: | The proposed ontologies are linked with the Ontologie of Linguistic Annotation and ISOCat, the GOLD ontology, the Typological Database Systems ontological and a large number of annotation schemes. |
A Large Harvested Corpus of Location Metonymy (2020.lrec-1)
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| Challenge: | Existing datasets of metonymy are either too small in size or lack sufficient coverage. |
| Approach: | They propose a new corpus of location metonymy called WiMCor that is large in size and has high coverage. |
| Outcome: | The proposed corpus is large in size and has high coverage. |
The DAPRECO Knowledge Base: Representing the GDPR in LegalRuleML (2020.lrec-1)
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| Challenge: | The DAPRECO knowledge base is a repository of rules written in LegalRuleML . the rules represent the provisions of the General Data Protection Regulation (GDPR) |
| Approach: | The DAPRECO knowledge base is a repository of rules written in LegalRuleML . the rules represent the provisions of the General Data Protection Regulation . |
| Outcome: | The DAPRECO knowledge base is the biggest knowledge base in LegalRuleML freely available online at (Robaldo et al., 2019). |
The Universal Decompositional Semantics Dataset and Decomp Toolkit (2020.lrec-1)
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Aaron Steven White, Elias Stengel-Eskin, Siddharth Vashishtha, Venkata Subrahmanyan Govindarajan, Dee Ann Reisinger, Tim Vieira, Keisuke Sakaguchi, Sheng Zhang, Francis Ferraro, Rachel Rudinger, Kyle Rawlins, Benjamin Van Durme
| Challenge: | Decompositional semantics is a method of crowd-sourcing semantic annotations while retaining high interannotator agreement. |
| Approach: | They present the Universal Decompositional Semantics dataset (v1.0) they propose a decomposition-aligned approach to semantic annotation that uses simple questions to answer . |
| Outcome: | The dataset is bundled with the Decomp toolkit (v0.1) both datasets are publicly available at http://decomp.io. |
Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit? (2020.lrec-1)
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| Challenge: | In recent years, vectors derived from neural network training have replaced count-based distributional semantic models as a de facto standard for word representation in NLP. |
| Approach: | They propose to evaluate count models and word embeddings on thematic fit estimation by taking into account a larger number of parameters and verb roles and introducing dependency-based embedders in the comparison. |
| Outcome: | The proposed model outperforms count models and word embeddings in thematic fit estimation tasks while introducing dependency-based embedders. |
Ciron: a New Benchmark Dataset for Chinese Irony Detection (2020.lrec-1)
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| Challenge: | Automatic Chinese irony detection often lacks labeled benchmark datasets . despite its pervasive nature, irony is a trope whose actual meaning differs from what is literally enunciated. |
| Approach: | They propose to use a Chinese benchmark dataset for automatic Chinese irony detection to provide a benchmark for machine learning models. |
| Outcome: | The proposed dataset includes more than 8.7K posts, collected from Weibo, a micro blogging platform. |
wikiHowToImprove: A Resource and Analyses on Edits in Instructional Texts (2020.lrec-1)
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| Challenge: | wikiHow articles are subject to revision edits, but do they provide clarifications? a new study compares changes made across multiple versions of the same set of instructions . |
| Approach: | They use wikiHow to analyze revision histories for 2.7 million sentences from wikihow . they use human annotation to categorize subset of edits and provide models . |
| Outcome: | The proposed model can distinguish between “older” and “newer” revisions of a sentence. |
Must Children be Vaccinated or not? Annotating Modal Verbs in the Vaccination Debate (2020.lrec-1)
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| Challenge: | In this paper we analyze the use of modal verbs in a corpus of texts related to the vaccination debate. |
| Approach: | They analyze the use of modal verbs in a corpus of texts related to the vaccination debate. |
| Outcome: | The use of modal verbs in the vaccination debate is analysed using modal auxiliaries and a corpus of texts. |
NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution (2020.lrec-1)
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| Challenge: | Negation is an important characteristic of language, and a major component of information extraction from text. |
| Approach: | They propose to use a popular transfer learning model to solve Negation Detection and Scope Resolution tasks in 3 datasets that have gained popularity over the years. |
| Outcome: | The proposed model outperforms existing systems on the BioScope Corpus, the Sherlock dataset and the SFU Review Corpus in scope resolution. |
Spatial Multi-Arrangement for Clustering and Multi-way Similarity Dataset Construction (2020.lrec-1)
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Olga Majewska, Diana McCarthy, Jasper van den Bosch, Nikolaus Kriegeskorte, Ivan Vulić, Anna Korhonen
| Challenge: | Existing methods for creating large-scale semantic similarity resources are slow and expensive . a large verb similarity dataset is available for a number of verbs, but not for English. |
| Approach: | They propose a method for fast bottom-up creation of large-scale semantic similarity resources . they leverage semantic intuitions of native speakers and adapt a spatial multi-arrangement approach to lexical stimuli. |
| Outcome: | The proposed approach produces a large-scale verb similarity dataset containing similarity scores for 29,721 unique verb pairs and 825 target verbs. |
A Short Survey on Sense-Annotated Corpora (2020.lrec-1)
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| Challenge: | Word Sense Disambiguation (WSD) is a key task in Natural Language Understanding. |
| Approach: | They propose to use sense-annotated corpora for supervised Word Sense Disambiguation. |
| Outcome: | The proposed methods have been compared with knowledge-based approaches and have shown to be more efficient when they are available. |
Using Distributional Thesaurus Embedding for Co-hyponymy Detection (2020.lrec-1)
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| Challenge: | Existing methods to detect lexical relations among distributionally similar words have been proposed to solve this problem. |
| Approach: | They propose to use distributional semantic models to detect co-hyponymy relations by embedding them into the distributional thesaurus. |
| Outcome: | The proposed model outperforms the state-of-the-art models for binary classification of co-hyponymy vs. hypernymy, as well as co-meronymy by huge margins. |
NUBes: A Corpus of Negation and Uncertainty in Spanish Clinical Texts (2020.lrec-1)
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| Challenge: | Currently, there are not many corpora that cover propositional aspects of meaning . these include factuality, uncertainty, opinions, beliefs, intentions or subjectivity . |
| Approach: | They introduce the first version of the NUBes corpus (Negation and Uncertainty annotations in Biomedical texts in Spanish) . it includes an exhaustive comparison with similar corpora in Spanish and preliminary experiments using deep learning algorithms to validate the annotated dataset. |
| Outcome: | The proposed corpus is compared with similar corpora in Spanish and performs preliminary experiments using deep learning algorithms. |
Decomposing and Comparing Meaning Relations: Paraphrasing, Textual Entailment, Contradiction, and Specificity (2020.lrec-1)
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| Challenge: | SHARel is a new typology for decomposing and comparing multiple meaning relations . it consists of 26 linguistic and 8 reason-based categories and can be applied to all relations with a high inter-annotator agreement. |
| Approach: | They propose a new typology that consists of 26 linguistic and 8 reason-based categories and propose SHARel for decomposing and comparing multiple meaning relations. |
| Outcome: | The proposed method can be applied to all relations with high inter-annotator agreement. |
Object Naming in Language and Vision: A Survey and a New Dataset (2020.lrec-1)
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| Challenge: | Object naming has been studied in Psycholinguistics, but has received little attention in Computational Linguistics. |
| Approach: | They propose a dataset that provides 36 name annotations for each of 25K objects in images selected from VisualGenome. |
| Outcome: | The proposed dataset shows that people choose certain names for objects, on average. |
MSD-1030: A Well-built Multi-Sense Evaluation Dataset for Sense Representation Models (2020.lrec-1)
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| Challenge: | Existing benchmarks for sense embeddings do not account for polysemy, and there are six issues with evaluations based on these datasets. |
| Approach: | They propose a multi-sense dataset with a high ratio of multi-word pairs to address the polysemy issue in word embeddings. |
| Outcome: | The proposed model performs better than existing models with single-sense word pairs and has a high ratio of multi-sensor word pairs. |
Figure Me Out: A Gold Standard Dataset for Metaphor Interpretation (2020.lrec-1)
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| Challenge: | Metaphor comprehension and understanding is a complex cognitive task that requires interpreting metaphors by grasping the interaction between the meaning of their target and source concepts. |
| Approach: | They propose an automatic retrieval approach to annotate verb-noun metaphors in text . they validated their approach by annotating around 1,500 metaphors from tweets . |
| Outcome: | The proposed method reduces the workload on annotators and maintains consistency . it can be used to interpret verb-noun metaphoric expressions in tweets . |
Extrinsic Evaluation of French Dependency Parsers on a Specialized Corpus: Comparison of Distributional Thesauri (2020.lrec-1)
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| Challenge: | Using a frequency-based method, we can identify subsets of the same word contexts without any reference data. |
| Approach: | They compare 11 different French dependency parsers on a specialized corpus to generate distributional thesauri using a frequency-based method. |
| Outcome: | The proposed method can identify relevant subsets without reference data and the similarity is confirmed on a restricted distributional benchmark. |
Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing (2020.lrec-1)
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| Challenge: | Clinical trials require that patients meet eligibility criteria to ensure safety and effectiveness of studies. |
| Approach: | They propose a dataset that includes the first-of-its-kind eligibility-criteria corpus and queries for criteria-to-sql . they propose 'neuro semantic parser' which can translate eligibility criteria to executable SQL queries . |
| Outcome: | The proposed parser outperforms existing state-of-the-art general-purpose models while highlighting the challenges presented by the new dataset. |
Recognizing Semantic Relations by Combining Transformers and Fully Connected Models (2020.lrec-1)
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| Challenge: | Current approaches to recognizing semantic relations between words are limited and require a word-path model. |
| Approach: | They propose a distributional approach that is based on an attention-based transformer and a word path model that combines useful properties of a convolutional network with a fully connected language model. |
| Outcome: | The proposed model outperforms the state-of-the-art in terms of performance and data sources. |
Word Attribute Prediction Enhanced by Lexical Entailment Tasks (2020.lrec-1)
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| Challenge: | a semantic attribute is associated with a designated dimension in attribute-based vector representations . semantic attributes are created by psychological experimental settings involving human annotators . a conceptual attribute of a concept dictates a specific semantic aspect of the concept . |
| Approach: | They propose a two-stage neural network architecture that fine-tunes attribute representations by employing supervised entailment tasks. |
| Outcome: | The proposed method improves performance of semantic/visual similarity/relatedness evaluation tasks. |
From Spatial Relations to Spatial Configurations (2020.lrec-1)
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| Challenge: | Existing spatial representations are not sufficient for describing complex spatial configurations. |
| Approach: | They propose to integrate existing spatial representation languages with an annotation schema to extend the capabilities of existing ones. |
| Outcome: | The proposed language can represent a large set of spatial concepts crucial for reasoning . it integrates with the Abstract Meaning Representation (AMR) annotation schema and annotates text from diverse datasets . |
Representing Verbs with Visual Argument Vectors (2020.lrec-1)
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| Challenge: | Existing models for verb semantic similarities are based on linguistic data, but they do not register intuitive attributes. |
| Approach: | They evaluated two textual distributional semantic models and a visual one to explore verb semantic similarities. |
| Outcome: | The proposed models extract meaningful information and capture semantic similarity between verbs using visual distributional models. |
Are White Ravens Ever White? - Non-Literal Adjective-Noun Phrases in Polish (2020.lrec-1)
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| Challenge: | FigAN data is a collection of isolated phrases with only literal and metaphorical meanings . FigSen corpus contains 1833 short fragments of texts containing at least one phrase from Figan data . |
| Approach: | They describe two resources of Polish data focused on literal and metaphorical meanings of adjective-noun phrases. |
| Outcome: | The proposed methods are compared with FigAN and FigSen corpus in Polish . the authors show that the methods are more accurate and more accurate than previous methods . |
CoSimLex: A Resource for Evaluating Graded Word Similarity in Context (2020.lrec-1)
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Carlos Santos Armendariz, Matthew Purver, Matej Ulčar, Senja Pollak, Nikola Ljubešić, Mark Granroth-Wilding
| Challenge: | Existing methods to evaluate word embeddings ignore context and treat words in isolation. |
| Approach: | They propose to build a new word embeddings-based dataset that provides context-dependent similarity measures. |
| Outcome: | The proposed dataset provides context-dependent similarity measures and covers a well-resourced language (English) but a number of less-resource languages. |
A French Version of the FraCaS Test Suite (2020.lrec-1)
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| Challenge: | a French version of the FraCaS test suite is presented in this paper . it contains problems illustrating semantic inference in natural language . |
| Approach: | They propose to test the NLP system's semantic capacity against inferencing tasks by translating the FraCaS test suite into French and running an experiment to test both the translation and the logical semantics underlying the problems. |
| Outcome: | The proposed tests were compared with similar tests conducted in other languages and show that the results are comparable to those of other tests. |
Automatic Compilation of Resources for Academic Writing and Evaluating with Informal Word Identification and Paraphrasing System (2020.lrec-1)
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| Challenge: | a systematic review of academic writing aids aims to build a writing aid system that automatically edits a text to adhere to the academic style of writing. |
| Approach: | They propose to build a writing aid system that automatically edits a text to adhere to the academic style of writing. |
| Outcome: | The proposed system outperforms existing academic resources in terms of word identification and ranking . the informal word identification component achieves an F-1 score of 82% . |
Sense-Annotated Corpora for Word Sense Disambiguation in Multiple Languages and Domains (2020.lrec-1)
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| Challenge: | Word Sense Disambiguation (WSD) is a field of NLP where data is usually tied to a specific language. |
| Approach: | They propose to release five large datasets annotated with word-senses in five different languages and 5 datasets in English for a different semantic domain. |
| Outcome: | The study shows that supervised models trained on the data achieve higher performance than those trained on other corpora. |
FrSemCor: Annotating a French Corpus with Supersenses (2020.lrec-1)
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Lucie Barque, Pauline Haas, Richard Huyghe, Delphine Tribout, Marie Candito, Benoit Crabbé, Vincent Segonne
| Challenge: | a new project aims to provide a sense-annotated corpus of French for NLP and linguistics research . the project uses WordNet Unique Beginners as semantic tags to provide interoperability . |
| Approach: | They propose to use WordNet Unique Beginners as semantic tags to annotate French nouns . the project aims to provide a gold standard resource for linguistics and linguistic research . |
| Outcome: | The proposed resource is released online under a Creative Commons license. |
A Formal Analysis of Multimodal Referring Strategies Under Common Ground (2020.lrec-1)
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| Challenge: | a recent study has focused on multimodality in the CL/NLP community, but it has not been widely studied. |
| Approach: | They propose to analyze mixed-modality definite referring expressions using gestures and linguistic descriptions. |
| Outcome: | The proposed models can predict viewer judgment of referring expressions and generate more natural and informative expressions. |
Improving Neural Metaphor Detection with Visual Datasets (2020.lrec-1)
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| Challenge: | a new method for metaphor detection uses text from visual datasets to identify words . a metaphor is a complex interaction between two terms, creating an "implicationcomplex" |
| Approach: | They propose a technique for sampling text from visual datasets to create a visibility word embedding. |
| Outcome: | The proposed method improves on previous approaches that use more complex neural networks and richer linguistic features for verb classification. |
Building a Hebrew Semantic Role Labeling Lexical Resource from Parallel Movie Subtitles (2020.lrec-1)
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| Challenge: | Existing semantic role labeling resources for Hebrew are not available in English. |
| Approach: | They propose a semantic role labeling resource for Hebrew built semi-automatically through annotation projection from English to Hebrew. |
| Outcome: | The proposed resource is built semi-automatically from an English dataset . it includes morphological analysis, dependency syntax and semantic role labeling . |
Word Sense Disambiguation for 158 Languages using Word Embeddings Only (2020.lrec-1)
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Varvara Logacheva, Denis Teslenko, Artem Shelmanov, Steffen Remus, Dmitry Ustalov, Andrey Kutuzov, Ekaterina Artemova, Chris Biemann, Simone Paolo Ponzetto, Alexander Panchenko
| Challenge: | Existing methods of disambiguation of word senses are based on knowledge bases, taxonomies, and other externally built resources. |
| Approach: | They propose a method that takes a pre-trained word embedding model and induces a fully-fledged word sense inventory for 158 languages. |
| Outcome: | The proposed model is based on a pre-trained word embedding model and induces a fully-fledged word sense inventory in 158 languages. |
Extraction of Hyponymic Relations in French with Knowledge-Pattern-Based Word Sketches (2020.lrec-1)
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| Challenge: | Hyponymy is the cornerstone of taxonomies and concept hierarchies. |
| Approach: | They propose a French hyponymic sketch grammar for Sketch Engine based on knowledge patterns. |
| Outcome: | The proposed grammar extracts hyponymic pairs from any user-owned corpus. |
SeCoDa: Sense Complexity Dataset (2020.lrec-1)
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| Challenge: | Sense Complexity Dataset (SeCoDa) provides a corpus that is annotated jointly for word senses and word tokens. |
| Approach: | They propose to use a hierarchical sense annotation scheme that draws on information available in the Cambridge Advanced Learner's Dictionary to provide more coarse-grained senses than WordNet. |
| Outcome: | The Sense Complexity Dataset (SeCoDa) provides a corpus that is annotated jointly for complexity and word senses. |
A New Resource for German Causal Language (2020.lrec-1)
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| Challenge: | Annotations of causal language are challenging for automatic and human annotators. |
| Approach: | They propose a German causal annotation resource with annotations in context for verbs, nouns and prepositions. |
| Outcome: | The proposed annotation scheme distinguishes three types of causal events . the proposed framework also provides annotations for semantic roles and actors . |
One Classifier for All Ambiguous Words: Overcoming Data Sparsity by Utilizing Sense Correlations Across Words (2020.lrec-1)
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| Challenge: | Existing word-specific classifiers lack the ability to generalize across words and require limited sense-annotated data for every word. |
| Approach: | They propose to learn a single model that derives sense representations and enforces congruence between a word instance and its right sense by using both sense-annotated data and lexical resources. |
| Outcome: | Empirical evaluation shows the proposed model outperforms classifier-based models by 1.7%, 2.5% and 3.8% in F1-score on GloVe, ELMo and BERT word embeddings respectively. |
A Corpus of Adpositional Supersenses for Mandarin Chinese (2020.lrec-1)
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| Challenge: | Adpositions are frequent markers of semantic relations, but they are highly ambiguous and vary significantly from language to language. |
| Approach: | They propose to annotate Chinese adpositions in a corpus with all aforementioned supersenses . they adapt a framework that defined a set of supersens according to ostensibly language-independent criteria . |
| Outcome: | The proposed corpus is the first to be broadly annotated with adposition semantics in Chinese . it shows that the supersense categories are well-suited to Chinese adepositions despite syntactic differences from English . |
The Russian PropBank (2020.lrec-1)
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| Challenge: | Using proposition bank for Russian, we can automatically project semantic role labels from English to Russian. |
| Approach: | They propose a proposition bank for Russian that automatically projects semantic role labels from English to Russian. |
| Outcome: | The proposed resource automatically projectes semantic role labels from English to Russian. |
What Comes First: Combining Motion Capture and Eye Tracking Data to Study the Order of Articulators in Constructed Action in Sign Language Narratives (2020.lrec-1)
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| Challenge: | a temporal order of articulators in overt constructed action is variable between different sequences and signers. |
| Approach: | They use synchronized 120 fps motion capture and 50 fpm eye tracking data to investigate temporal order in which dominant hand, head, chest and eyes produce overt constructed action from regular narration. |
| Outcome: | The proposed analysis shows that the temporal order of articulators shows contextual and individual variation but also repeats across all the analyzed sequences and signers. |
LSF-ANIMAL: A Motion Capture Corpus in French Sign Language Designed for the Animation of Signing Avatars (2020.lrec-1)
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| Challenge: | Signing avatars are often procedurally animated, resulting in robotic and unnatural movements, which are therefore rejected by the Deaf community. |
| Approach: | They propose to use a French Sign Language corpus to create an avatar that can be edited from motion capture data to create new signs and utterances. |
| Outcome: | The proposed corpus is based on a french Sign Language (LSF) corpus composed of captured signs and sentences. |
Sign Language Recognition with Transformer Networks (2020.lrec-1)
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| Challenge: | Sign language recognition is a complex problem, supported by large video corpora . previous work has used feature extraction or end-to-end deep learning to speed annotation . |
| Approach: | They propose to use OpenPose for human keypoint estimation and Convolutional Neural Networks to extract sign language features from video corpora. |
| Outcome: | The proposed method outperforms the state-of-the-art on the Flemish Sign Language corpus. |
Annotating a Fable in Italian Sign Language (LIS) (2020.lrec-1)
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| Challenge: | fables are short or medium-length stories with a moral and they generally have specific characteristics in SLs that are usually not to be found in spoken languages like Italian. |
| Approach: | They present work for automatic generation of a written text in Italian starting from glosses of fable in Italian Sign Language (LIS). |
| Outcome: | The proposed method was used to generate a written text in Italian starting from glosses of a fable in Italian Sign Language (LIS). |
HamNoSyS2SiGML: Translating HamNoSys Into SiGML (2020.lrec-1)
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| Challenge: | Sign Languages are visual languages and the main means of communication used by Deaf people. |
| Approach: | They propose to create a tool that can convert HamNoSys to SiGML and perform synthetic animations of conversational agents using notation systems. |
| Outcome: | The proposed tool can perform conversion from HamNoSys to SiGML independently from other platforms. |
Dicta-Sign-LSF-v2: Remake of a Continuous French Sign Language Dialogue Corpus and a First Baseline for Automatic Sign Language Processing (2020.lrec-1)
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| Challenge: | Existing research on automatic Sign Language Processing (SLP) has focused on recognizing lexical signs, but other gestural units like iconic structures need to be recognized. |
| Approach: | They propose a public remake of the French Sign Language part of the Dicta-Sign corpus with clean annotations and a Convolutional-Recurrent Neural Network to train and test it. |
| Outcome: | The proposed version of the publicly available SL corpus Dicta-Sign is limited to its French Sign Language part and includes lexical and non-lexical annotations over 11 hours of video recording with 35000 manual units. |
An HMM Approach with Inherent Model Selection for Sign Language and Gesture Recognition (2020.lrec-1)
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| Challenge: | despite the extensive use of HMMs for sign recognition, determining the HMM structure remains a challenge . despite their success in modeling sequential and multivariate data, establishing the structure remains challenging . |
| Approach: | They propose a continuous HMM framework for modeling and recognizing isolated signs . they propose to optimize the number of states for each sign separately during recognition . |
| Outcome: | The proposed model performs better on three different datasets and is competitive with existing models. |
VROAV: Using Iconicity to Visually Represent Abstract Verbs (2020.lrec-1)
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| Challenge: | Visual languages like sign languages reveal enlightening patterns across signs of similar meanings, pointing towards the possibility of identifying clusters of iconic meanings. |
| Approach: | a new verb classification system is proposed to visually represent 20 classes of abstract verbs. |
| Outcome: | The proposed system could be used as a language learning aid or as linguistic comprehension tool for digital text. |
MEDIAPI-SKEL - A 2D-Skeleton Video Database of French Sign Language With Aligned French Subtitles (2020.lrec-1)
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| Challenge: | MEDIAPI-SKEL is a 2D-skeleton database of french Sign Language videos aligned with French subtitles. |
| Approach: | They propose a 2D-skeleton database of french Sign Language videos aligned with French subtitles and a vocabulary size of 17k tokens. |
| Outcome: | The proposed corpus contains 27 hours of video of body, face and hand keypoints, aligned to subtitles with a vocabulary size of 17k tokens. |
Alignment Data base for a Sign Language Concordancer (2020.lrec-1)
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| Challenge: | a new study examines the need for sign language translators to have tools similar to text-to-text translation. |
| Approach: | They propose to use a concordancer to search for parallel Franch-LSF segments . they use dozens of short news clips and 120 SL videos to align them manually . |
| Outcome: | The proposed data base will be searched using a concordancer and expand in the future. |
Evaluation of Manual and Non-manual Components for Sign Language Recognition (2020.lrec-1)
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Medet Mukushev, Arman Sabyrov, Alfarabi Imashev, Kenessary Koishybay, Vadim Kimmelman, Anara Sandygulova
| Challenge: | Deaf communities communicate via sign languages to express meaning and intent. |
| Approach: | They used sign samples from 20 commonly used signs in Kazakh-Russian Sign Language (K-RSL) to investigate whether non-manual components would improve sign’s recognition accuracy. |
| Outcome: | The results showed that using non-manual components would improve sign recognition accuracy. |
TheRuSLan: Database of Russian Sign Language (2020.lrec-1)
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| Challenge: | The database is the first of a kind for Russian sign language and is intended for use in machine learning, gesture recognition and sign language linguistics. |
| Approach: | They present a Russian sign language multimedia database called TheRuSLan . the database includes lexical units from Russian sign languages within one subject area . |
| Outcome: | The proposed database includes lexical units from Russian sign language within one subject area. |
A Survey on Natural Language Processing for Fake News Detection (2020.lrec-1)
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| Challenge: | Automated fake news detection is a critical but challenging problem in NLP . social media has accelerated the spread of fake news, threatening public safety . |
| Approach: | They describe the challenges involved in fake news detection and describe related tasks . they outline promising research directions and highlight the difference between fake news and related tasks. |
| Outcome: | The proposed models are more fine-grained, detailed, fair, and practical. |
RP-DNN: A Tweet Level Propagation Context Based Deep Neural Networks for Early Rumor Detection in Social Media (2020.lrec-1)
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| Challenge: | Existing methods for early rumor detection on social media platforms are limited, incomplete and noisy. |
| Approach: | They propose a novel hybrid neural network architecture which combines a task-specific character-based bidirectional language model and stacked Long Short-Term Memory (LSTM) networks to represent textual contents and social-temporal contexts of input source tweets. |
| Outcome: | The proposed model achieves state-of-the-art for detecting unseen rumors on large augmented data which covers more than 12 events and 2,967 rumors. |
Issues and Perspectives from 10,000 Annotated Financial Social Media Data (2020.lrec-1)
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| Challenge: | In the NLP community, many researchers have begun to use machine learning on financial and economic data. |
| Approach: | They present a dataset with 10,000 financial tweets annotated by experts from the front desk and the middle desk in a bank’s treasury. |
| Outcome: | The annotated financial tweets of a bank's front desk and middle desk are compared against a general sentiment dictionary and a domain-specific dictionary. |
Searching Brazilian Twitter for Signs of Mental Health Issues (2020.lrec-1)
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| Challenge: | Existing resources are largely devoted to English NLP, and there is little support for these studies in under resourced languages. |
| Approach: | They propose to build a corpus in Brazilian Portuguese to support both the recognition of mental health issues and the temporal analysis of these illnesses. |
| Outcome: | The proposed corpus will support both the recognition of mental health issues and the temporal analysis of these illnesses in the Brazilian Portuguese language. |
RedDust: a Large Reusable Dataset of Reddit User Traits (2020.lrec-1)
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| Challenge: | Social media is a rich source of assertions about personal traits, but identifying personal traits from implicit assertions is difficult because of the users’ highly varied vocabulary and expressions. |
| Approach: | They propose to build a large-scale annotated resource for user profiling for over 300k Reddit users across five attributes: profession, hobby, family status, age, and gender. |
| Outcome: | The proposed resource is the first annotated language resource about Reddit users at large scale. |
An Annotated Social Media Corpus for German (2020.lrec-1)
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| Challenge: | Hate Speech (HS) against ethnic, religious and national minorities is a growing concern in online discourse. |
| Approach: | They present the German Twitter section of a large (2 billion word) bilingual Social Media corpus for Hate Speech research. |
| Outcome: | The proposed parser achieved F-scores of 97% for morphology and 92% for syntax on a cross-section of tweets. |
The rJokes Dataset: a Large Scale Humor Collection (2020.lrec-1)
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| Challenge: | Humor is a complex language phenomenon that depends upon many factors, including topic, date, and recipient. |
| Approach: | They compile a large scale humor dataset from the Reddit r/Jokes subreddit. |
| Outcome: | The proposed dataset provides quantitative metrics for the level of humor in each joke, as determined by subreddit user feedback. |
EmpiriST Corpus 2.0: Adding Manual Normalization, Lemmatization and Semantic Tagging to a German Web and CMC Corpus (2020.lrec-1)
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| Challenge: | EmpiriST corpus is a manually tokenized and part-of-speech tagged corpus of German web and CMC data. |
| Approach: | They extend the corpus with manually tokenized annotation layers for word form normalization, lemmatization and lexical semantics. |
| Outcome: | The EmpiriST corpus is a manually tokenized and part-of-speech tagged corpus of German web and CMC data. |
Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection (2020.lrec-1)
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| Challenge: | Prior fake news datasets lack multimodal text and image data, metadata, comment data, and fine-grained classification at the scale and breadth of their datasets. |
| Approach: | They propose to use a multimodal dataset to build a machine learning classification model that uses text and image data to classify fake news. |
| Outcome: | The proposed model is based on a multimodal dataset consisting of over 1 million samples from multiple categories of fake news. |
Optimising Twitter-based Political Election Prediction with Relevance andSentiment Filters (2020.lrec-1)
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| Challenge: | A set of over 17,000 tweets containing political party names were annotated by at least three annotators per tweet on ten features denoting communicative intent. |
| Approach: | They propose to annotate tweets containing political party names by using oracle filters to achieve lower MAEs. |
| Outcome: | The proposed method achieves a mean absolute error (MAE) of 2.71% for 2012 and 2.02% for 2012, and 2.89% for 2015 for the Dutch elections. |
A Real-Time System for Credibility on Twitter (2020.lrec-1)
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| Challenge: | Using neural networks, we can analyze Twitter in real-time to determine whether users are credible and false. |
| Approach: | They propose to analyze Twitter in real-time using neural networks to determine credibility of tweets and users who posted them. |
| Outcome: | The proposed method analyzes Twitter in real-time to determine which users are credible and which are not, what is false or what is true on the Internet. |
A Corpus of Turkish Offensive Language on Social Media (2020.lrec-1)
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| Challenge: | Identifying abusive, offensive, aggressive or in general inappropriate language has recently attracted interest of researchers from academic as well as commercial institutions. |
| Approach: | They propose to classify Turkish offensive language corpus using state-of-the-art annotation methods . they find 19 % of tweets contain some type of offensive language . |
| Outcome: | The proposed corpus of Turkish offensive language is the first of its kind in the world . the results show that 19 % of the tweets contain some type of offensive language . |
From Witch’s Shot to Music Making Bones - Resources for Medical Laymen to Technical Language and Vice Versa (2020.lrec-1)
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| Challenge: | Information we share online unveils directly or indirectly information about our lifestyle and health situation. |
| Approach: | They propose a dataset which annotates medical laymen and technical expressions in a patient forum and a set of medical synonyms and definitions. |
| Outcome: | The proposed dataset annotates medical laymen and technical expressions in a patient forum along with a set of medical synonyms and definitions. |
I Feel Offended, Don’t Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language (2020.lrec-1)
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| Challenge: | Recent literature suggests different approaches to identify abusive language phenomena . however, there is a lack of data sets that take into account the degree of explicitness . |
| Approach: | They propose to use annotation guidelines to distinguish between explicit and implicit abuse in English and apply them to OLID/OffensEval. |
| Outcome: | The proposed tool distinguishes between explicit and implicit abuse in English and takes into account the degree of explicitness. |
A Multi-Platform Arabic News Comment Dataset for Offensive Language Detection (2020.lrec-1)
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Shammur Absar Chowdhury, Hamdy Mubarak, Ahmed Abdelali, Soon-gyo Jung, Bernard J. Jansen, Joni Salminen
| Challenge: | Social media platforms allow users to engage in conversation with limited accountability, causing hate crimes and mental harm to targeted individuals. |
| Approach: | They propose to make public a new dialectal Arabic news comment dataset . they analyze distinctive lexical content along with the use of emojis in offensive comments . |
| Outcome: | The proposed dataset analyzes offensive language and distinctive lexical content along with the use of emojis on Twitter, Facebook, and YouTube. |
Twitter Trend Extraction: A Graph-based Approach for Tweet and Hashtag Ranking, Utilizing No-Hashtag Tweets (2020.lrec-1)
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Zahra Majdabadi, Behnam Sabeti, Preni Golazizian, Seyed Arad Ashrafi Asli, Omid Momenzadeh, Reza Fahmi
| Challenge: | Twitter has become a major platform for users to express their opinions on any topic and engage in debates. |
| Approach: | They propose to use tweets as graph nodes to extract trends from tweets graph . they propose to employ RankClus algorithm to rank tweets, words and hashtags in each trend . |
| Outcome: | The proposed algorithm can extract trends from tweets and rank tweets, words and hashtags based on their importance and relevance to the topic. |
A French Corpus for Event Detection on Twitter (2020.lrec-1)
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| Challenge: | Existing datasets may have different definitions of event or topic, which leads to inconsistent results. |
| Approach: | They present a corpus annotated for event detection tasks consisting of 38 million tweets in French and 130,000 manually annotating tweets as related or unrelated to a given event. |
| Outcome: | The proposed method performs best on 38 million tweets in French and another publicly available dataset of tweets. |
Minority Positive Sampling for Switching Points - an Anecdote for the Code-Mixing Language Modeling (2020.lrec-1)
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| Challenge: | Multilingual people code-mix using English phonetic typing and insertion of anglicisms in their native language. |
| Approach: | They propose to use minority positive sampling to selectively induce more sample to achieve better performance. |
| Outcome: | The proposed model performs better than other models, but switching points are the main challenge . |
Do You Really Want to Hurt Me? Predicting Abusive Swearing in Social Media (2020.lrec-1)
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| Challenge: | Swearing is a common form of verbal communication and occurs in social media and online forums . a study by a team of researchers has investigated the phenomenon of swearing in Twitter . |
| Approach: | They analyze tweets to determine abusive swearing using models that automatically predict it . they also investigate lexical, syntactic, and affective features that are more informative . |
| Outcome: | The proposed model can predict abusive swearing in a tweet context and provide an intrinsic evaluation of the model. |
Detecting Troll Tweets in a Bilingual Corpus (2020.lrec-1)
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| Challenge: | a large amount of troll accounts have emerged with efforts to manipulate public opinion on social network sites . a recent study found that trolled tweets spread misinformation, fake news, and propaganda . we use supervised classification to detect trol tweets in both English and Russian . |
| Approach: | They propose to detect troll tweets in English and Russian using machine learning algorithms . they use monolingual, cross-lingual, and bilingual training scenarios . |
| Outcome: | The proposed method uses monolingual, cross-lingual, and bilingual training scenarios. |
Collecting Tweets to Investigate Regional Variation in Canadian English (2020.lrec-1)
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| Challenge: | 78.8 million tweets, 1.3 billion words, and a focus on the dialect regions of Toronto, Montreal, and Vancouver are included in this study. |
| Approach: | They propose to use a 78.8-million-tweet, 1.3-billion-word corpus to study regional variation in Canadian English with a focus on the dialect regions of Toronto, Montreal, and Vancouver. |
| Outcome: | The proposed corpus mirrors national and regional specificities of Canadian English and provides sufficient aggregate and user-level data and maintains a reasonably balanced distribution of content across regions and users. |
DAICT: A Dialectal Arabic Irony Corpus Extracted from Twitter (2020.lrec-1)
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| Challenge: | Current scholarship is yet to reach an agreement on a universal definition of the concept of irony. |
| Approach: | They propose to query Twitter using irony-related hashtags to collect ironic messages which are then manually annotated by two linguists according to their working definition of irony. |
| Outcome: | The proposed corpus will be a valuable resource for developing open domain systems for automatic irony recognition in Arabic and its dialects in social media text. |
Norm It! Lexical Normalization for Italian and Its Downstream Effects for Dependency Parsing (2020.lrec-1)
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| Challenge: | Existing tools for lexical normalization of social media data are designed with canonical texts in mind, and this makes it difficult to process data in multiple languages. |
| Approach: | They propose to create a lexical normalization dataset for Italian and analyze the inter-annotator agreement for this task. |
| Outcome: | The proposed model improves the parsing of social media data in Italian and shows that it can be used to translate non-standard social media content to canonical language. |
TArC: Incrementally and Semi-Automatically Collecting a Tunisian Arabish Corpus (2020.lrec-1)
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| Challenge: | Arabish is a spontaneous coding of Arabic dialects in Latin characters and "arithmographs" this code-system was developed by Arabic-speaking users of social media . little research has been dedicated to Tunisian Arabish (TA) |
| Approach: | They describe the constitution process of the first morpho-syntactically annotated Tunisian Arabish Corpus . they describe preliminary work on the TArC semi-automatic construction process . |
| Outcome: | The first morpho-syntactically annotated Tunisian Arabish corpus (TArC) was developed by arab-speaking users of social media . the code-system will be a useful support for different types of analyses, computational and linguistic, as well as for NLP tools training. |
Small Town or Metropolis? Analyzing the Relationship between Population Size and Language (2020.lrec-1)
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| Challenge: | Prior studies have examined how location affects the type of language that people use . recent electoral results in the united states exemplify a divide in the political opinions of those living in densely populated areas . |
| Approach: | They analyze tweets from different Twitter users to determine whether they are from an urban or rural area. |
| Outcome: | The proposed model trains predictive models to predict whether a user is from an urban or rural area. |
Inferring Social Media Users’ Mental Health Status from Multimodal Information (2020.lrec-1)
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| Challenge: | In the United States alone, one in every four adults suffers from a mental health condition, making mental health a pressing concern. |
| Approach: | They propose to use multimodal cues present in social media posts to predict mental health status by analyzing language, visual, and metadata cue data. |
| Outcome: | The proposed approach improves the performance of the classification task compared to using one modality at a time and can provide important cues into a user’s mental status. |
Synthetic Data for English Lexical Normalization: How Close Can We Get to Manually Annotated Data? (2020.lrec-1)
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| Challenge: | Social media data is a valuable data resource for natural language processing tasks. |
| Approach: | They propose to adapt input text to a more standard form, a task also referred to as normalization. |
| Outcome: | The proposed system scores 94.29 accuracy on the test data compared to 95.22 when trained on human-annotated data. |
A Corpus of German Reddit Exchanges (GeRedE) (2020.lrec-1)
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| Challenge: | Reddit is a popular online platform combining social news aggregation, discussion and microblogging. |
| Approach: | They propose a method to filter out German data and further pre-processing steps to find out what is linguistically peculiar in the German data. |
| Outcome: | The proposed method filters out German data and includes metadata and annotation layers. |
French Tweet Corpus for Automatic Stance Detection (2020.lrec-1)
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| Challenge: | a new corpus of tweets is being developed for automatic stance detection of fake news . the task involves determining the attitude expressed in a text toward a target . this is a difficult task to overcome as discussions about fake news are controversial . |
| Approach: | They propose to build a human-annotated corpus for automatic stance detection of tweets in french . they propose to use four classes broadly adopted by the community for annotation . |
| Outcome: | The proposed corpus is the first freely available stance annotated tweet corpus in the french language. |
LSCP: Enhanced Large Scale Colloquial Persian Language Understanding (2020.lrec-1)
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| Challenge: | a gap exists in describing low-resource formal languages such as Persian . a large scale corpus of 120M sentences is proposed to fill this gap . |
| Approach: | They propose to target a gap in describing the colloquial language for low-resource ones such as Persian . a large scale Persian corpus is hierarchically organized in a semantic taxonomy . |
| Outcome: | The proposed corpus consists of 120M sentences from 27M tweets annotated with parsing tree, part-of-speech tags, sentiment polarity and translation in five different languages. |
Burmese Speech Corpus, Finite-State Text Normalization and Pronunciation Grammars with an Application to Text-to-Speech (2020.lrec-1)
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Yin May Oo, Theeraphol Wattanavekin, Chenfang Li, Pasindu De Silva, Supheakmungkol Sarin, Knot Pipatsrisawat, Martin Jansche, Oddur Kjartansson, Alexander Gutkin
| Challenge: | Using crowd-sourced speech corpus and finite-state transducer grammars, we build a text-to-speech system for Burmese, a tonal Southeast Asian language from the Sino-Tibetan family. |
| Approach: | They propose an open-source crowd-sourced multi-speaker speech corpus and finite-state grammars for performing grapheme-to-phoneme conversion for Burmese. |
| Outcome: | The proposed system performs well for Burmese in a low-resource setting. |
Evaluating and Improving Child-Directed Automatic Speech Recognition (2020.lrec-1)
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| Challenge: | a recent study shows that adult speech recognition systems are lagging behind child models due to the fact that children's vocal tracts are smaller than adults . |
| Approach: | They evaluate a model that trains on adult data and apply additional tuning to varied amounts of child speech data to improve child-directed speech recognition. |
| Outcome: | The proposed model improves over baseline models using child data and small amounts of child audio data. |
Parallel Corpus for Japanese Spoken-to-Written Style Conversion (2020.lrec-1)
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| Challenge: | spoken-to-written style conversion is becoming an important technology to increase the readability of ASR transcriptions. |
| Approach: | They propose to build a Japanese parallel corpus of spoken-to-written style conversions . they use crowdsourcing to convert spoken-style text into written-style texts . |
| Outcome: | The proposed corpus can handle general and specific spoken-to-written style conversion problems in Japanese. |
Multi-Staged Cross-Lingual Acoustic Model Adaption for Robust Speech Recognition in Real-World Applications - A Case Study on German Oral History Interviews (2020.lrec-1)
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| Challenge: | Current automatic speech recognition systems show remarkable performance when adequate data is used for training. |
| Approach: | They propose to perform a robust acoustic model adaption to a target domain in a cross-lingual manner. |
| Outcome: | The proposed approach reduces word error rate by more than 30% on German oral history interviews compared to a model trained from scratch on the target domain and 6-7% on same-language out-of-domain training data. |
Large Corpus of Czech Parliament Plenary Hearings (2020.lrec-1)
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| Challenge: | a corpus of Czech parliament plenary sessions is a valuable resource for future research . only a few public datasets are available in the Czech language . end-to-end approaches require extensive training data to produce competitive results . |
| Approach: | They present a corpus of Czech parliament plenary sessions which is a large corpus . they combine a traditional approach with a more traditional approach . |
| Outcome: | The proposed model architectures can be used to train and evaluate speech recognition systems on a large corpus of speech data and transcripts. |
Augmented Prompt Selection for Evaluation of Spontaneous Speech Synthesis (2020.lrec-1)
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| Challenge: | Spontaneous speech is unscripted and created on the fly by the speaker, whereas read speech is pre-planned. |
| Approach: | They propose a tool that allows developers to select a varied, representative set of utterances from a spoken genre to be used for evaluation of TTS for a given domain. |
| Outcome: | The proposed tool can be used to evaluate TTS for a given domain using visualisation and tree-based algorithm. |
ATC-ANNO: Semantic Annotation for Air Traffic Control with Assistive Auto-Annotation (2020.lrec-1)
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| Challenge: | ATC communications are a challenging domain for automatic speech recognition (ASR) due to the time-sensitive nature of their task, annotators must have prior experience with ATC communication. |
| Approach: | They propose a tool for the transcription and semantic annotation of air traffic communications. |
| Outcome: | The proposed tool can annotate four times as many utterances in a single time. |
MASRI-HEADSET: A Maltese Corpus for Speech Recognition (2020.lrec-1)
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Carlos Daniel Hernandez Mena, Albert Gatt, Andrea DeMarco, Claudia Borg, Lonneke van der Plas, Amanda Muscat, Ian Padovani
| Challenge: | Maltese is the national language of Malta and is spoken by approximately 500,000 people. |
| Approach: | They present the first spoken Maltese corpus designed purposely for Automatic Speech Recognition (ASR) it consists of 8 hours of speech paired with text, recorded by using short text snippets in a laboratory environment. |
| Outcome: | The MASRI-HEADSET corpus was developed by the MASR project at the University of Malta. |
Automatic Period Segmentation of Oral French (2020.lrec-1)
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| Challenge: | Analor is a semi-automatic tool for speech segmentation in periods but it only takes into account prosodic characteristics of speech. |
| Approach: | They propose to use a Fribourg model of macro-syntax to detect periods in syntactic and prosodic terms to develop an automatic tool for automatic segmentation of linguistic units. |
| Outcome: | The proposed tool is compared with an existing tool Analor which divides speech into smaller segments and that CRF models detect larger segments rather than macro-syntactic periods. |
Corpus Generation for Voice Command in Smart Home and the Effect of Speech Synthesis on End-to-End SLU (2020.lrec-1)
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| Challenge: | Massive amounts of annotated data are often unavailable for novel tasks performed in real-world environments such as smart homes. |
| Approach: | They propose to use a synthetic semantically-annotated corpus of French commands for smart-home to train pipeline and end-to-end (E2E) SLU models. |
| Outcome: | The proposed model trains pipeline and end-to-end (E2E) SLU models on voice commands acquired in a real smart home. |
Text and Speech-based Tunisian Arabic Sub-Dialects Identification (2020.lrec-1)
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| Challenge: | Dialect IDentification is a difficult task when it is about the identification of dialects belonging to the same country. |
| Approach: | They present results on a dialect classification task covering four sub-dialects spoken in Tunisia using a spoken corpus of 1673 utterances. |
| Outcome: | The proposed system achieves an F-1 score of 93.75% while the F-1 is limited to 54.16% using text-based DID on the same test set. |
Urdu Pitch Accents and Intonation Patterns in Spontaneous Conversational Speech (2020.lrec-1)
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| Challenge: | Recent studies of Urdu intonation describe scripted and laboratory speech . |
| Approach: | They summarise Urdu pitch accents and their intonation patterns using a simplified version of the Rhythm and Pitch labelling system and a simple RAP system. |
| Outcome: | The analysis of a hand-labelled telephone conversation shows that low pitch accents play an important role in Urdu spontaneous speech. |
IndicSpeech: Text-to-Speech Corpus for Indian Languages (2020.lrec-1)
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| Challenge: | India has 22 languages, each of them being spoken by over a million people . the current state of the art text-to-speech systems for Indian languages are lacking in the multimedia domain . |
| Approach: | They propose to train a state-of-the-art TTS system for Hindi, Malayalam and Bengali and publish the results. |
| Outcome: | The proposed system trains neural text-to-speech systems for Hindi, Malayalam and Bengali and makes them publicly available. |
Using Automatic Speech Recognition in Spoken Corpus Curation (2020.lrec-1)
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| Challenge: | Automatic Speech Recognition (ASR) is a new way to make audio-visual data accessible. |
| Approach: | They propose to use automatic speech recognition (ASR) to make audio-visual data accessible by systematic queries. |
| Outcome: | The proposed system has higher recognition scores for the north of Germany vs. lower scores for south of the country. |
Integrating Disfluency-based and Prosodic Features with Acoustics in Automatic Fluency Evaluation of Spontaneous Speech (2020.lrec-1)
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| Challenge: | acoustics, prosody, and disfluency-based features are used to evaluate fluent/disfluent speech . filling pauses and word fragments are used for automatic fluency evaluation . |
| Approach: | They integrate acoustics, prosody, and disfluency-based features into an automatic fluency evaluation task. |
| Outcome: | The proposed model improves when integrated with prosodic features, but not when disfluent speech is detected. |
DNN-based Speech Synthesis Using Abundant Tags of Spontaneous Speech Corpus (2020.lrec-1)
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Yuki Yamashita, Tomoki Koriyama, Yuki Saito, Shinnosuke Takamichi, Yusuke Ijima, Ryo Masumura, Hiroshi Saruwatari
| Challenge: | Experimental evaluation results show that rich annotations enhance the reproducibility of paralinguistic features of synthetic speech. |
| Approach: | They investigate the effectiveness of using rich annotations in deep neural network-based statistical speech synthesis. |
| Outcome: | The proposed method improves reproducibility of paralinguistic features of synthetic speech . the corpus of spontaneous Japanese (CSJ) has large annotations on paralinguistic and nonlinguistic features . |
Automatic Speech Recognition for Uyghur through Multilingual Acoustic Modeling (2020.lrec-1)
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| Challenge: | Low-resource languages suffer from lower performance of Automatic Speech Recognition (ASR) due to the lack of data. |
| Approach: | They propose to use Turkish as donor language to train acoustic models using multilingual training to achieve more context coverage. |
| Outcome: | The proposed system performs better with multilingual training for the under-resourced Uyghur language. |
The SAFE-T Corpus: A New Resource for Simulated Public Safety Communications (2020.lrec-1)
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| Challenge: | Linguistic Data Consortium developed the SAFE-T Corpus to support the NIST OpenSAT evaluation series. |
| Approach: | They introduce a new resource, the SAFE-T Corpus, designed to simulate first-responder communications by inducing high vocal effort and urgent speech with situational background noise. |
| Outcome: | The SAFE-T Corpus was developed to support the NIST OpenSAT (Speech Analytic Technologies) evaluation series. |
Lexical Tone Recognition in Mizo using Acoustic-Prosodic Features (2020.lrec-1)
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| Challenge: | Mizo is an under-studied Tibeto-Burman tonal language of the Northeast of India. |
| Approach: | They propose to use acoustic-prosodic parameters to automatically recognize four phonological tones in Mizo using a set of features computed from Fundamental Frequency contours. |
| Outcome: | The proposed model performs better than the existing classifiers in recognizing four phonological tones in Mizo using acoustic-prosodic parameters. |
Artie Bias Corpus: An Open Dataset for Detecting Demographic Bias in Speech Applications (2020.lrec-1)
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| Challenge: | A speech technology exhibits demographic bias when performance is worse for one demographic group relative to another. |
| Approach: | They create an English dataset of expert-validated audio, transcript> pairs with demographic tags for age, gender, accent and open software which may be used to detect demographic bias in Automatic Speech Recognition systems. |
| Outcome: | The Artie Bias Corpus is a curated subset of the Mozilla Common Voice corpus, which is released under a Creative Commons CC0 license . |
Evaluation of Off-the-shelf Speech Recognizers Across Diverse Dialogue Domains (2020.lrec-1)
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| Challenge: | a recent study evaluated off-the-shelf automatic speech recognition systems . current state-of-the art systems perform poorly in domains that require special vocabulary and language models . |
| Approach: | They evaluate off-the-shelf automatic speech recognition systems across different dialogue domains . they use data collected from deployed spoken dialogue systems and human-human conversations . |
| Outcome: | The evaluation is aimed at non-experts with limited experience in speech recognition . the results show that the performance of each speech recognizer can vary significantly depending on the domain . |
CEASR: A Corpus for Evaluating Automatic Speech Recognition (2020.lrec-1)
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Malgorzata Anna Ulasik, Manuela Hürlimann, Fabian Germann, Esin Gedik, Fernando Benites, Mark Cieliebak
| Challenge: | Automatic Speech Recognition (ASR) systems are increasingly needed for research and practical applications. |
| Approach: | They propose to use public speech corpora to evaluate the quality of automatic speech recognition (ASR) they calculate an average Word Error Rate (WER) per corpus, per system and per corpor-system pair . |
| Outcome: | The proposed corpus evaluates the quality of automatic speech recognition systems using public speech corpora and transcripts generated by state-of-the-art systems. |
MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible (2020.lrec-1)
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| Challenge: | The Bible is the same for all the languages, thus constituting a multilingual and comparable 2 spoken corpus, is not exploited to date. |
| Approach: | They propose to add multilingual links between small speech segments in different languages . they use a large dataset of 8,130 parallel spoken utterances across 8 languages - maSS . |
| Outcome: | The proposed model can build automatic speech recognition models for 700 languages. |
Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems (2020.lrec-1)
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Fei He, Shan-Hui Cathy Chu, Oddur Kjartansson, Clara Rivera, Anna Katanova, Alexander Gutkin, Isin Demirsahin, Cibu Johny, Martin Jansche, Supheakmungkol Sarin, Knot Pipatsrisawat
| Challenge: | We present free high quality multi-speaker speech corpora for Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu . the datasets are primarily intended for use in text-to-speech applications, such as constructing multilingual voices or language adaptation. |
| Approach: | They present a free high quality multi-speaker speech corpora for Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu . they use it to build a multilingual text-to-speech model that can be scaled to other languages of interest. |
| Outcome: | The proposed model produces good quality voices with MOS > 3.6 for all the languages tested. |
Crowdsourcing Latin American Spanish for Low-Resource Text-to-Speech (2020.lrec-1)
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Adriana Guevara-Rukoz, Isin Demirsahin, Fei He, Shan-Hui Cathy Chu, Supheakmungkol Sarin, Knot Pipatsrisawat, Alexander Gutkin, Alena Butryna, Oddur Kjartansson
| Challenge: | Using crowd-sourced datasets, we build a text-to-speech voice for a new dialect in a language with existing resources. |
| Approach: | They propose a multidialectal corpus approach for building a text-to-speech voice for a new dialect in a language with existing resources using crowd-sourcing. |
| Outcome: | The proposed model outperforms baseline models in a “zero-resource” dialect scenario while holding out target dialect recordings from the training data. |
A Manually Annotated Resource for the Investigation of Nasal Grunts (2020.lrec-1)
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| Challenge: | acoustic annotation of nasal grunts is described in the whole CID corpus of the french language . acculturation of non-lexical conversational sounds has been debated for a long time . |
| Approach: | They propose an annotation framework for nasal grunts of the whole French CID corpus . they characterise acoustic cues and visual cue conventions followed for the annotation . |
| Outcome: | The proposed framework is based on the entire French CID corpus. |
The Objective and Subjective Sleepiness Voice Corpora (2020.lrec-1)
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| Challenge: | Following chronic sleep disorders involves multiple appointments between doctors and patients which often results in episodic follow-ups with unevenly spaced interviews. |
| Approach: | They propose to use a large database to assess the sleepiness level of highly phenotyped patients that complain from excessive daytime sleepiness instead of healthy subjects. |
| Outcome: | The proposed model is based on recordings from patients suffering from excessive daytime sleepiness instead of healthy subjects and incites them to sleep contrary to existing stressing sleepiness deprivation paradigms. |
Open-source Multi-speaker Corpora of the English Accents in the British Isles (2020.lrec-1)
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| Challenge: | Using a dataset of high-quality audio, the authors examine the accents of 120 volunteers in the British Isles. |
| Approach: | They present a dataset of high-quality audio of English sentences recorded by volunteers with different accents of the British Isles. |
| Outcome: | The transcribed audio includes pronunciations of global locations, major airlines and common personal names in different accents. |
TV-AfD: An Imperative-Annotated Corpus from The Big Bang Theory and Wikipedia’s Articles for Deletion Discussions (2020.lrec-1)
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| Challenge: | Detecting imperatives in oral and written communication is difficult when the user doesn't use the expected forms. |
| Approach: | They created an imperative corpus with dialogues from The Big Bang Theory and Wikipedia comments from Wikipedia . they manually annotated imperatives and used a syntax-based classifier to extract 10,624 statements that may be imperative. |
| Outcome: | The proposed model performs better in the written data compared to speech data, but has a low precision and recall for speech data. |
A Large Scale Speech Sentiment Corpus (2020.lrec-1)
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| Challenge: | Existing corpus for sentiment analysis uses text inputs, but voice inputs are becoming more important as smart assistants and mobile voice control become more prevalent. |
| Approach: | They propose to extend the Switchboard-1 Telephone Speech Corpus by adding sentiment labels from 3 different human annotators for every transcript segment. |
| Outcome: | The proposed corpus contains 49500 labeled speech segments covering 140 hours of audio. |
SibLing Corpus of Russian Dialogue Speech Designed for Research on Speech Entrainment (2020.lrec-1)
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Tatiana Kachkovskaia, Tatiana Chukaeva, Vera Evdokimova, Pavel Kholiavin, Natalia Kriakina, Daniil Kocharov, Anna Mamushina, Alla Menshikova, Svetlana Zimina
| Challenge: | a corpus of dialogue speech is created specifically for research in the field of speech entrainment. |
| Approach: | They propose to use a corpus of dialogue speech for research in the field of speech entrainment. |
| Outcome: | The proposed corpus includes 5 types of relations between interlocutors . the researchers recorded 90 dialogues of 25-60 minutes each . |
PhonBank and Data Sharing: Recent Developments in European Portuguese (2020.lrec-1)
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| Challenge: | 84 104 105 115 32 112 97 112 101 114 32 111 114 101 99 101 110 116 115 116 100 99 108 117 100 101 118 101 108 101 113 113 99 103 117 101 115 101 32 102 114 111 109 32 80 111 112 112 1 111 101 101 109 108 100 101 101 101 100 118 117 103 110 115 99 117 99 116 101 116 99 107 |
| Approach: | 84 104 105 115 32 112 97 112 101 114 32 111 114 101 99 101 116 105 101 118 105 110 103 105 99 117 99 116 101 117 100 118 101 119 111 114 117 103 101 115 101 109 32 99 111 101 108 100 114 100 116 100 119 99 109 99 108 101 113 113 99 113 101 111 99 99 99 114 99 99 101 101 103 |
| Outcome: | 84 104 105 115 32 112 97 112 101 114 32 111 114 101 99 101 118 105 110 116 115 116 99 99 99 100 118 101 116 101 119 111 114 117 99 99 104 101 115 101 117 103 117 101 113 117 100 117 110 117 39 99 99 101 101 111 101 109 32 99 99 110 108 100 114 100 116 100 108 108 101 108 99 99 95 |
SMASH Corpus: A Spontaneous Speech Corpus Recording Third-person Audio Commentaries on Gameplay (2020.lrec-1)
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| Challenge: | Developing a spontaneous speech corpus is important for spoken language research . a corpus of spontaneous speech is needed to develop these techniques . |
| Approach: | They propose to use Japanese male commentators' spontaneous speech to construct a SMASH corpus . they use transcriptions and topic tags to annotate the commentaries and report some results . |
| Outcome: | The proposed corpus includes spontaneous speech of two Japanese male commentators . the authors report that the annotations yielded a better corpus than the previous methods . |
Improving Speech Recognition for the Elderly: A New Corpus of Elderly Japanese Speech and Investigation of Acoustic Modeling for Speech Recognition (2020.lrec-1)
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| Challenge: | In an aging society, a highly accurate speech recognition system is needed for use in electronic devices for the elderly but this cannot be achieved using conventional speech recognition systems due to the unique features of the speech of elderly people. |
| Approach: | They construct a new corpus of elderly Japanese speech from existing Japanese speech corpora and train them using existing data. |
| Outcome: | The proposed models achieve word error rates (WER) as low as 13.38%, exceeding the results of the previous study. |
Preparation of Bangla Speech Corpus from Publicly Available Audio & Text (2020.lrec-1)
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Shafayat Ahmed, Nafis Sadeq, Sudipta Saha Shubha, Md. Nahidul Islam, Muhammad Abdullah Adnan, Mohammad Zuberul Islam
| Challenge: | Automated speech recognition systems require large annotated speech corpus for training. |
| Approach: | They propose to use publicly available Bangla audiobooks and TV news recordings as input to prepare a large speech corpus with reasonable confidence. |
| Outcome: | The proposed algorithm outperforms the existing speech corpus and the existing corpus with speaker diarization and gender detection. |
On Construction of the ASR-oriented Indian English Pronunciation Dictionary (2020.lrec-1)
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| Challenge: | Indian English (IE) has distinctive characteristics, especially phonologically, from other varieties of English. |
| Approach: | They build a small IE spontaneous speech corpus and use a linguistically-guided IE pronunciation dictionary to apply it to IE. |
| Outcome: | The proposed system performs better on IE spontaneous speech data than the one trained with CMUdict. |
Gender Representation in Open Source Speech Resources (2020.lrec-1)
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| Challenge: | Using open source corpora, we find that gender balance depends on other corpus characteristics such as elicited/non ellicite vs. non-eliciting speech, low/high resource language, speech task targeted. |
| Approach: | They propose to use open source corpora to find gender information in spoken language systems . they propose metadata and recommendations for researchers to assure better transparency . |
| Outcome: | The proposed method improves the quality and transparency of open source speech resources. |
RSC: A Romanian Read Speech Corpus for Automatic Speech Recognition (2020.lrec-1)
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| Challenge: | Romanian language is under-resourced due to the lack of acoustic and linguistic resources. |
| Approach: | They propose to use a Romanian speech corpus to train automatic speech recognition algorithms based on the spoken hotword detection mechanism. |
| Outcome: | The read speech corpus is a speech recognition system that can perform automatic speech recognition and speech synthesis using state-of-the-art speech recognition toolkit. |
FAB: The French Absolute Beginner Corpus for Pronunciation Training (2020.lrec-1)
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| Challenge: | French Absolute Beginner corpus is intended for the development and study of Computer-Assisted Pronunciation Training (CAPT) tools for absolute beginner learners. |
| Approach: | They introduce the French Absolute Beginner (FAB) speech corpus which is intended for the development and study of Computer-Assisted Pronunciation Training tools for absolute beginner learners. |
| Outcome: | The proposed corpus is intended for the development and study of Computer-Assisted Pronunciation Training tools for absolute beginner learners. |
Call My Net 2: A New Resource for Speaker Recognition (2020.lrec-1)
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| Challenge: | Call My Net 2 (CMN2) corpus features Tunisian Arabic conversations between friends and family . call recordings include speech in various realistic and natural acoustic settings, both noisy and non-noisy. |
| Approach: | They introduce the Call My Net 2 (CMN2) corpus, a new resource for speaker recognition featuring Tunisian Arabic conversations between friends and family. |
| Outcome: | The Call My Net 2 (CMN2) corpus contains data from over 400 Tunisian Arabic speakers . each speaker made 10 or more calls each lasting up to 10 minutes . |
DaCToR: A Data Collection Tool for the RELATER Project (2020.lrec-1)
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| Challenge: | Obtaining sufficient amount of data is often a problem for low-resource languages, such as dialects or non-written languages. |
| Approach: | They propose to collect domain-specific data in Arabic by collecting read texts by speakers in the respective countries and districts in which the dialects are spoken. |
| Outcome: | The proposed tool collects read texts by speakers in the countries and districts in which the dialects are spoken. |
Development and Evaluation of Speech Synthesis Corpora for Latvian (2020.lrec-1)
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| Challenge: | Recent advances in neural speech synthesis have enabled the development of text to speech systems for all languages. |
| Approach: | They propose to obtain a suitable corpus from unannotated Latvian audio recordings using automated speech recognition and speaker segmentation and identification. |
| Outcome: | The proposed method and software tools are applied and evaluated on a Latvian public radio archive data. |
Abstractive Document Summarization without Parallel Data (2020.lrec-1)
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| Challenge: | Abstractive summarization typically relies on large collections of paired articles and summaries. |
| Approach: | They propose a system that relies only on example summaries and non-matching articles . they use an unsupervised sentence extractor that selects salient sentences . |
| Outcome: | The proposed system performs well on CNN/DailyMail benchmark and automatic generating a press release from a scientific journal article. |
GameWikiSum: a Novel Large Multi-Document Summarization Dataset (2020.lrec-1)
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| Challenge: | Existing datasets contain only hundreds of samples, resulting in heavy reliance on hand-crafted features or manually annotated data. |
| Approach: | They propose a new domain-specific dataset for multi-document summarization that is 100 times larger than commonly used datasets. |
| Outcome: | The proposed dataset is 100 times larger than commonly used datasets and in another domain than news. |
Summarization Corpora of Wikipedia Articles (2020.lrec-1)
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| Challenge: | Using Wikipedia articles, we extract summarization data for other languages. |
| Approach: | They propose a process to extract Wikipedia summarization corpora and apply it to the German language. |
| Outcome: | The proposed method can be applied to the German language and compares to baselines. |
Language Agnostic Automatic Summarization Evaluation (2020.lrec-1)
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| Challenge: | Existing evaluation methods for summarization of documents have been primarily focused on the English language. |
| Approach: | They propose to use ROUGE and PYRAMID to evaluate non-English data using English and non- English data sets. |
| Outcome: | The proposed evaluation methods can be adapted to non-English data, and the results show that they can perform well on non- English data. |
Two Huge Title and Keyword Generation Corpora of Research Articles (2020.lrec-1)
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| Challenge: | Recent advances in sequence-to-sequence learning with neural networks have improved the quality of automatically generated text summaries and document keywords. |
| Approach: | They propose to use OAGSX and OAGKX datasets to analyze text summaries and document keywords. |
| Outcome: | The proposed models perform better than previous models on two large datasets . the authors hope to use the results to derive subsets of research articles from more disciplines . |
A Multi-level Annotated Corpus of Scientific Papers for Scientific Document Summarization and Cross-document Relation Discovery (2020.lrec-1)
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| Challenge: | Recent studies have proposed to take advantage of the scientific paper's citation network to approach literature summarization. |
| Approach: | They propose to annotate related work sections, cite papers and sentences using machine readable data and an additional layer of papers citing the references. |
| Outcome: | The proposed corpus expands the existing data-set of related work sections and cites the papers cited in the related work section. |
Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling (2020.lrec-1)
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Dmitrii Aksenov, Julian Moreno-Schneider, Peter Bourgonje, Robert Schwarzenberg, Leonhard Hennig, Georg Rehm
| Challenge: | Abstractive summarization is an NLP task with many real-world applications. |
| Approach: | They propose to use a pre-trained language model to train a Transformer-based neural model . they propose a new method of BERT-windowing to allow chunk-wise processing of texts longer than the BERT window size . |
| Outcome: | The proposed model outperforms baseline models on CNN/Daily Mail dataset and shows its superiority on German dataset. |
A Data Set for the Analysis of Text Quality Dimensions in Summarization Evaluation (2020.lrec-1)
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| Challenge: | Existing methods for summarization evaluation focus on a metric to represent the quality of the text, but they focus on only a few quality dimensions. |
| Approach: | They analyze the depen-dencies between various quality dimensions on automatically created multi-document summaries and which are best suited for summarization. |
| Outcome: | The proposed method achieves higher quality summaries than other methods on a large-scale heterogeneous data set. |
Summarization Beyond News: The Automatically Acquired Fandom Corpora (2020.lrec-1)
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| Challenge: | Abstractive summarization methods require large corpora to train neural architectures. |
| Approach: | They propose a novel automatic corpus construction approach that automatically constructs large open-licensed summarization corpora from existing large text collections and an evaluation process with human annotators. |
| Outcome: | The proposed approach can be used to train abstractive summarization models on large corpora and through a manual evaluation with human annotators. |
Invisible to People but not to Machines: Evaluation of Style-aware HeadlineGeneration in Absence of Reliable Human Judgment (2020.lrec-1)
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| Challenge: | Using a data alignment strategy and different training/testing settings, we aim at decoupling content from style and preserving the latter in generation. |
| Approach: | They propose a fine-grained evaluation strategy based on automatic classification to evaluate generated headlines' quality in terms of their newspaper-compliance. |
| Outcome: | The proposed model learns newspaper-specific style, but humans aren't reliable judges for this task, and deserves particular care in its design. |
Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation (2020.lrec-1)
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| Challenge: | Summarizing text is not a straightforward task. |
| Approach: | They propose to use automated transcriptions to generate reports from automatic transcriptions as a dataset for neural summarization. |
| Outcome: | The proposed model improves on publicmeetings corpus on a dataset of aligned public meetings. |
A Summarization Dataset of Slovak News Articles (2020.lrec-1)
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| Challenge: | a number of studies on document summarization have focused on the English language . however, most of the work on this task is done on English datasets . |
| Approach: | They propose to use a news site's ROUGE metric to adapt it to Slovak texts . they propose to introduce a large-scale news-based summarization dataset . |
| Outcome: | The proposed approach is better suited for Slovak texts than the dominant ROUGE metric. |
DaNewsroom: A Large-scale Danish Summarisation Dataset (2020.lrec-1)
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| Challenge: | Existing datasets for automatic summarisation are English-oriented . however, only very limited datasets exist in languages other than English . |
| Approach: | They present the first large-scale non-English dataset specifically curated for automatic summarisation. |
| Outcome: | The proposed dataset is the first for the Danish language and is compared with existing datasets. |
Diverging Divergences: Examining Variants of Jensen Shannon Divergence for Corpus Comparison Tasks (2020.lrec-1)
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| Challenge: | Jensen-Shannon divergence (JSD) is a distribution similarity measurement widely used in natural language processing. |
| Approach: | They propose to use a weighted version of Jensen-Shannon divergence to compare corpora . they argue this weighting is unnecessary and can lead to misleading results . |
| Outcome: | The proposed weighting is unnecessary and can lead to misleading results. |
TopicNet: Making Additive Regularisation for Topic Modelling Accessible (2020.lrec-1)
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Victor Bulatov, Vasiliy Alekseev, Konstantin Vorontsov, Darya Polyudova, Eugenia Veselova, Alexey Goncharov, Evgeny Egorov
| Challenge: | TopicNet is a Python module for topic modeling. |
| Approach: | They introduce a Python module for topic modeling that brings regularization topic modeling to non-specialists using a general-purpose language. |
| Outcome: | The proposed module aims to bring topic modeling to non-specialists using a general-purpose language. |
SC-CoMIcs: A Superconductivity Corpus for Materials Informatics (2020.lrec-1)
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| Challenge: | Existing corpus of superconducting materials in Materials Informatics (MI) is limited. |
| Approach: | They propose to create a corpus tailored for the text mining of superconducting materials in Materials Informatics. |
| Outcome: | The proposed corpus can find terms relevant to a query term within a specified Named Entity category. |
GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors (2020.lrec-1)
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| Challenge: | Lack of large-scale datasets has been a major hindrance to the development of NLP tasks such as spelling correction and grammatical error correction. |
| Approach: | They propose to make GitHub Typo Corpus a multilingual dataset of misspellings and grammatical errors available for use in NLP. |
| Outcome: | The proposed dataset contains more than 350k edits and 65M characters in more than 15 languages. |
Annotation of Adverse Drug Reactions in Patients’ Weblogs (2020.lrec-1)
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| Challenge: | Adverse drug reactions are a severe problem that significantly degrade quality of life and make the therapeutic approach unacceptable. |
| Approach: | They crawled patient’s weblog articles shared on an online patient-networking platform and annotated the effects of drugs therein reported. |
| Outcome: | The proposed dataset is unique for the richness of annotated information, including detailed descriptions of drug reactions with full context. |
Beyond Citations: Corpus-based Methods for Detecting the Impact of Research Outcomes on Society (2020.lrec-1)
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| Challenge: | Existing methods for assessing the impact of research are ineffective for identifying impact beyond academia and text-based indicators beyond those that capture attention. |
| Approach: | They propose a deductive and inductive approach to categorize research impact categories using a corpus-based approach . they use a combination of deductive methods and machine learning to infer impact categories from project reports. |
| Outcome: | The proposed method predicts deductively and inductively derived impact categories with 76.39% accuracy and 78.81% accuracy. |
Toxic, Hateful, Offensive or Abusive? What Are We Really Classifying? An Empirical Analysis of Hate Speech Datasets (2020.lrec-1)
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| Challenge: | a recent study shows that many definitions are being used for equivalent concepts, making most datasets incompatible. |
| Approach: | They analyze six publicly available datasets to determine their similarity and compatibility . they propose to use Fast Text word vectors to analyze similarity between different datasets . |
| Outcome: | The proposed model performs better on similar datasets and worse on more non-offensive samples. |
Unsupervised Argumentation Mining in Student Essays (2020.lrec-1)
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| Challenge: | State-of-the-art argumentation mining systems rely on annotated training data and are supervised, thus relying on an annotation of the components and relationships between them. |
| Approach: | They propose to bootstrap from a small set of argument components automatically identified using simple heuristics in combination with reliable contextual cues. |
| Outcome: | The proposed approach outperforms two supervised baselines and achieves 73.5-83.7% of the performance of a state-of-the-art neural approach. |
Aspect-Based Sentiment Analysis as Fine-Grained Opinion Mining (2020.lrec-1)
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| Challenge: | a large body of research has been done on aspect-based sentiment analysis (ABSA) for almost two decades . aspect-Based sentiment analysis is a task that extracts sentiment/opinions from text in terms of targets . |
| Approach: | They propose a meaning-preserving annotation scheme for aspect-based sentiment analysis . they then apply it to two popular ABSA datasets to examine their results . |
| Outcome: | The proposed approach improves the state of aspect-based sentiment analysis (ABSA) by preserving the meaning of the sentiment. |
Predicting Item Survival for Multiple Choice Questions in a High-Stakes Medical Exam (2020.lrec-1)
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| Challenge: | Existing methods of pretesting are costly and require a large pool of test questions to be replenished, updated and expanded over time. |
| Approach: | They propose to automatically predict an item's probability to "survive" pretesting by embedding new items within a live exam and analyzing the responses. |
| Outcome: | The proposed method is based on human-produced MCQs for a medical exam and shows that survival is modelled through linguistic features and embedding types and features inspired by information retrieval. |
Discourse Component to Sentence (DC2S): An Efficient Human-Aided Construction of Paraphrase and Sentence Similarity Dataset (2020.lrec-1)
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| Challenge: | a dataset of similar sentences and paraphrases is a challenging task, but it requires high resources. |
| Approach: | They propose a discourse component-based paraphrase generation scheme for directive utterances . they use Korean language, which has diverse paraphrasing due to frequent subject drop . |
| Outcome: | The proposed scheme is efficient in terms of human-aided construction and content preservation. |
Japanese Realistic Textual Entailment Corpus (2020.lrec-1)
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| Challenge: | a corpus of 48,000 realistic examples is the largest among publicly available Japanese TE corpora . a textual entailment corpus is used to train natural language understanding . authors: to be truly helpful, machines must understand the meaning of texts. |
| Approach: | They perform textual entailment corpus construction with 48,000 realistic examples . they use two sentences that are spontaneous or almost equivalent . |
| Outcome: | The resulting corpus consists of 48,000 realistic Japanese examples . it is the largest among publicly available Japanese TE corpora . |
Improving the Precision of Natural Textual Entailment Problem Datasets (2020.lrec-1)
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| Challenge: | a new method to modify natural textual entailment problem datasets is proposed . we aim to examine to what extent existing corpora for NLI capture precise reasoning . |
| Approach: | They propose to modify natural textual entailment problem datasets to reflect more precise notion of entropy. |
| Outcome: | The proposed method can be used to test precise natural-language inference systems . it uses crowd-sourced experts to make any missing hypotheses explicit . |
Comparative Study of Sentence Embeddings for Contextual Paraphrasing (2020.lrec-1)
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| Challenge: | Paraphrasing is an important aspect of natural-language generation that can produce more variety in the way specific content is presented. |
| Approach: | They propose to use contextual paraphrasing to capture the meaning of a sentence while performing dialogue act clustering. |
| Outcome: | The proposed task combines paraphrases with dialogue act clustering to capture such contextual paraphrasing. |
HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference (2020.lrec-1)
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| Challenge: | Recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by looking at the hypothesis while completely ignoring the premise. |
| Approach: | They propose to derive adversarial examples in terms of the hypothesis-only bias and explore eligible ways to mitigate such bias. |
| Outcome: | The proposed models can be used to mitigate the hypothesis-only bias by using down-sampling and adversarial training. |
SAPPHIRE: Simple Aligner for Phrasal Paraphrase with Hierarchical Representation (2020.lrec-1)
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| Challenge: | Monolingual phrase alignment is a fundamental problem in natural language understanding and crucial technique in various applications. |
| Approach: | They propose a simple Aligner for Phrasal Paraphrase with HIerarchical REpresentation that uses word embeddings to train phrase alignments. |
| Outcome: | The proposed algorithm outperforms the previous methods and establishes the state-of-the-art. |
TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages (2020.lrec-1)
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| Challenge: | a crowdsourcing project aimed at language learners has created a paraphrase corpus for 73 languages . the corpus contains 1.9 million sentences, with 200 - 250 000 sentences per language . |
| Approach: | They propose to use a Tatoeba-based dataset to create a paraphrase corpus for 73 languages. |
| Outcome: | The proposed dataset contains 1.9 million sentences and 200 - 250 000 sentences per language. |
Automated Fact-Checking of Claims from Wikipedia (2020.lrec-1)
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| Challenge: | Fact checking datasets such as FEVER and SNLI suffer from limited applicability due to synthetic nature of claims and/or evidence written by annotators that differ from real claims and evidence on the internet. |
| Approach: | They present a dataset of 124k+ triples consisting of a claim, context and an evidence document extracted from English Wikipedia articles and citations. |
| Outcome: | The proposed dataset is the largest fact checking dataset consisting of real claims and evidence to date. |
Towards the Necessity for Debiasing Natural Language Inference Datasets (2020.lrec-1)
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| Challenge: | Delexicalization of datasets can improve natural language inference performance . a dataset with a delexicalized version of the FEVER dataset is used for natural language learning . |
| Approach: | They propose two techniques for delexicalization that modify annotated datasets to control the importance of lexical entities. |
| Outcome: | The proposed methods maintain performance in-domain and improve performance in some out-of-domain settings. |
A French Corpus for Semantic Similarity (2020.lrec-1)
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| Challenge: | Semantic textual similarity is a subtask of Natural Language Processing. |
| Approach: | They propose to use an annotation corpus for French to assess semantic similarity . they use an annotated corpus with 1,010 sentence pairs with five annotators . |
| Outcome: | The proposed corpus for French is the first that we know of. |
Developing Dataset of Japanese Slot Filling Quizzes Designed for Evaluation of Machine Reading Comprehension (2020.lrec-1)
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| Challenge: | a novel dataset of slot filling quizzes is developed for evaluation of machine reading comprehension . the dataset consists of quizzed characters of target books as slots . |
| Approach: | They develop a Japanese dataset of slot filling quizzes for machine reading comprehension . they define each quiz as a 4-tuple: context passage, query holding a slot, answer character and set of possible answer characters . |
| Outcome: | The proposed dataset includes a 4-tuple quiz, a context passage, q and a query . the q&a is performed on characters of target books, which play important roles in narrative texts . |
Detecting Negation Cues and Scopes in Spanish (2020.lrec-1)
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Salud María Jiménez-Zafra, Roser Morante, Eduardo Blanco, María Teresa Martín Valdivia, L. Alfonso Ureña López
| Challenge: | Negation is a phenomenon that "relates an expression e to another expression with a meaning that is in some way opposed to the meaning of e" previous work on negation in English has focused mostly and only recently on annotation tasks. |
| Approach: | They propose a machine learning system that processes negation in Spanish . they use a corpus from the SFU corpus to perform two tasks . |
| Outcome: | The proposed system outperforms state-of-the-art in negation cue detection and scope identification. |
TIARA: A Tool for Annotating Discourse Relations and Sentence Reordering (2020.lrec-1)
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| Challenge: | Existing tools for discourse relations and sentence reordering are difficult to use and clutter the display. |
| Approach: | They propose to use TIARA to simplify the annotation process by offering interactive visualisation, including coloured links, indentation, and dual-view. |
| Outcome: | The proposed tool simplifies the annotation process and offers visualisations including coloured links, indentation, and dual-view. |
Infrastructure for Semantic Annotation in the Genomics Domain (2020.lrec-1)
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Mahmoud El-Haj, Nathan Rutherford, Matthew Coole, Ignatius Ezeani, Sheryl Prentice, Nancy Ide, Jo Knight, Scott Piao, John Mariani, Paul Rayson, Keith Suderman
| Challenge: | a novel infrastructure for biomedical text mining combines NLP and corpus linguistics methods to provide a comprehensive corpus for literature-based discovery. |
| Approach: | They propose a novel pipeline for the collection, annotation, storage, retrieval and analysis of biomedical and life sciences literature . it uses an updatable Gene Ontology Semantic Tagger and a NLP pipeline scheduler to collect and process the corpus. |
| Outcome: | The proposed infrastructure allows for extreme-scale research on the open access PubMed Central archive. |
Correcting the Autocorrect: Context-Aware Typographical Error Correction via Training Data Augmentation (2020.lrec-1)
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| Challenge: | a recent study shows that typographical errors are now ubiquitous . traditional spelling correction software is inadequate to correct typographical mistakes . |
| Approach: | They propose to generate typographical errors based on annotated spelling errors . they then use annotations to introduce errors into substantially larger corpora . |
| Outcome: | The proposed method generates typographical errors that require context-aware error detection . it also shows that machine learning can correct typographical mistakes based on the data . |
KidSpell: A Child-Oriented, Rule-Based, Phonetic Spellchecker (2020.lrec-1)
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Brody Downs, Oghenemaro Anuyah, Aprajita Shukla, Jerry Alan Fails, Sole Pera, Katherine Wright, Casey Kennington
| Challenge: | Existing spellcheckers are tuned to the needs of adults and are unsatisfactory for children due to their varying cognitive capabilities. |
| Approach: | They propose a model that maps misspelled words and spelling suggestions to their phonetic keys and a selection process that prioritizes candidate spelling suggestions that closely align with the misspelled word. |
| Outcome: | The proposed model outperforms existing spellcheckers in a number of offline experiments using existing and novel datasets. |
ThaiLMCut: Unsupervised Pretraining for Thai Word Segmentation (2020.lrec-1)
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Suteera Seeha, Ivan Bilan, Liliana Mamani Sanchez, Johannes Huber, Michael Matuschek, Hinrich Schütze
| Challenge: | ThaiLMCut is a semi-supervised word segmentation model for word segmenting in Thai . it uses a bi-directional character language model to leverage useful linguistic knowledge from unlabeled data. |
| Approach: | They propose a semi-supervised approach to Thai word segmentation using a character language model. |
| Outcome: | The proposed approach outperforms state-of-the-art models on the benchmark InterBEST2009. |
CCOHA: Clean Corpus of Historical American English (2020.lrec-1)
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| Challenge: | Existing methods to model language change in diachronic studies have been used to overcome its limitations. |
| Approach: | They propose to use the corpus of historical american english to overcome its limitations . they use a downloadable version of the corpora to remove inconsistent lemmas and malformed tokens . |
| Outcome: | The proposed corpus overcomes its main limitations without compromising its qualitative and distributional properties. |
Outbound Translation User Interface Ptakopět: A Pilot Study (2020.lrec-1)
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| Challenge: | a task called outbound translation is not uncommon for Internet users to have to produce a text in a foreign language they have very little knowledge of and are unable to verify the translation quality. |
| Approach: | They propose an open-source modular system to inspect human interaction with machine translation systems enhanced with additional subsystems such as backward translation and quality estimation. |
| Outcome: | The proposed system is able to produce a text in a foreign language with minimal knowledge and is compared with MT systems of mid-range quality. |
Seshat: a Tool for Managing and Verifying Annotation Campaigns of Audio Data (2020.lrec-1)
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Hadrien Titeux, Rachid Riad, Xuan-Nga Cao, Nicolas Hamilakis, Kris Madden, Alejandrina Cristia, Anne-Catherine Bachoud-Lévi, Emmanuel Dupoux
| Challenge: | Seshat is a software for the automated management of annotation campaigns for audio/speech data. |
| Approach: | They propose a system for the automated management of annotation campaigns for audio/speech data which addresses these challenges. |
| Outcome: | The proposed system computes an associated inter-annotator agreement with the gamma measure taking into account the categorisation and segmentation discrepancies. |
Dragonfly: Advances in Non-Speaker Annotation for Low Resource Languages (2020.lrec-1)
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| Challenge: | Using semantic and contextual information, non-speakers of a language familiar with the Latin script can produce high quality named entity annotations to support construction of . name tagger. |
| Approach: | They propose a procedure for annotating low resource languages using Dragonfly that others can use. |
| Outcome: | The proposed procedure improves the performance of NER models on native speaker and non-speaker annotations in low resource languages. |
Natural Language Processing Pipeline to Annotate Bulgarian Legislative Documents (2020.lrec-1)
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| Challenge: | The Bulgarian MARCELL corpus consists of 25,283 documents, which are classified into eleven types. |
| Approach: | They present the Bulgarian MARCELL corpus, part of a newly developed multilingual corpus representing the national legislation in seven European countries. |
| Outcome: | The proposed corpus represents the national legislation in seven European countries and the NLP pipeline that turns the web crawled data into structured, linguistically annotated dataset. |
CLDFBench: Give Your Cross-Linguistic Data a Lift (2020.lrec-1)
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| Challenge: | despite the increasing amount of cross-linguistic data, most datasets are not FAIR (findable, accessible, interoperable, and reproducible) . with the Cross-Linguistic Data Formats initiative, first standards for cross-language data have been presented and successfully tested. |
| Approach: | They propose a framework for the retro-standardization of legacy data and the curation of new datasets that drastically simplifies the creation of CLDFs. |
| Outcome: | The proposed framework simplifies the creation of CLDFs by providing a consistent, reproducible workflow that supports version control and long term archiving of research data and code. |
KonText: Advanced and Flexible Corpus Query Interface (2020.lrec-1)
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| Challenge: | KonText is a corpus query interface built on top of core NoSketch Engine core libraries. |
| Approach: | KonText is built on top of core NoSketch Engine core libraries . it provides integration capabilities allowing connection of basic corpus search service with other languages . |
| Outcome: | The proposed interface is built on top of core libraries of the open-source corpus search engine NoSketch Engine (NoSkE) it overcomes some limitations and provides integration capabilities with other language resources. |
Word at a Glance: Modular Word Profile Aggregator (2020.lrec-1)
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| Challenge: | Word at a Glance (WaG) is a word profile aggregator that provides means for exploring individual words, their comparison and translation. |
| Approach: | They propose a word profile aggregator that aggregates data from different sources into a single web page. |
| Outcome: | The proposed application is designed as a building kit-like application that fetches data from different sources and compiles it into a single, comprehensible and structured web page. |
RKorAPClient: An R Package for Accessing the German Reference Corpus DeReKo via KorAP (2020.lrec-1)
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| Challenge: | RKorAPClient provides access to large annotated corpora including the German reference corpus DeReKo with 45 billion tokens. |
| Approach: | They propose to integrate KorAP with an R package that interacts with KorAP API . the package provides access to large annotated corpora including the German reference corpus DeReKo . |
| Outcome: | The proposed tool provides access to large annotated corpora including the German reference corpus DeReKo with 45 billion tokens. |
CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing (2020.lrec-1)
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Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, Nizar Habash
| Challenge: | CAMeL Tools provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and sentiment analysis. |
| Approach: | They present CAMeL Tools, an open-source Python toolkit for Arabic natural language processing . CAMeleL Tools provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and sentiment analysis. |
| Outcome: | The proposed tools are based on CAMeL Tools, an open-source Python toolkit for Arabic natural language processing. |
ReSiPC: a Tool for Complex Searches in Parallel Corpora (2020.lrec-1)
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| Challenge: | a tool specifically designed to allow for complex searches in large parallel corpora is presented . the tool is able to POS-tag the corporum, but it can be easily adapted for other kinds of information. |
| Approach: | They propose a tool specifically designed to allow for complex searches in large parallel corpora . they use standard regular expressions that combine word forms, lemmata and POS-tags . |
| Outcome: | The proposed tool can perform complex searches in large parallel corpora using POS-tags . it can be used to provide data for contrastive linguistics research . |
HitzalMed: Anonymisation of Clinical Text in Spanish (2020.lrec-1)
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| Challenge: | HITZALMED is a web-framed tool that performs automatic detection of sensitive information in clinical texts using machine learning algorithms reported to be competitive for the task. |
| Approach: | This paper presents a web-framed tool that performs automatic detection of sensitive information in clinical texts using machine learning algorithms reported to be competitive for the task. |
| Outcome: | The proposed tool is available online and can be configured by the user. |
The xtsv Framework and the Twelve Virtues of Pipelines (2020.lrec-1)
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| Challenge: | Numerous small independent tools do not fit in a larger framework, and they may not be acknowledged properly. |
| Approach: | They propose an abstract framework for building NLP pipelines which is based on xtsv and a tsvo format with header plus some additional features. |
| Outcome: | The proposed framework is based on a concrete application for a Hungarian NLP pipeline. |
A Web-based Collaborative Annotation and Consolidation Tool (2020.lrec-1)
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| Challenge: | Annotation tools have a rigid structure, closed back-end and front-end, and are built in a non-user-friendly way rendering them unusable for a large cohort. |
| Approach: | They propose a web-based collaborative annotation and consolidation tool (AWOCATo) that supports varied textual formats and allows users to easily adapt to the annotation task. |
| Outcome: | AWOCATo supports a range of tasks and domains, filling the gap left by the lack of tools that can be used by people with and without programming knowledge. |
Data Query Language and Corpus Tools for Slot-Filling and Intent Classification Data (2020.lrec-1)
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| Challenge: | Typical machine learning approaches require large amounts of training data . Managing training data can be cumbersome without dedicated tools . |
| Approach: | They propose a toolkit for analyzing slot-filling and intent classification corpora . they propose 'Query Language' for searching such corporan and tools for understanding structure . |
| Outcome: | The proposed toolkit can be used to uncover interesting and surprising insights. |
SHR++: An Interface for Morpho-syntactic Annotation of Sanskrit Corpora (2020.lrec-1)
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| Challenge: | SHR++ is designed to generate annotations for word segmentation, morphological parsing and dependency analysis tasks in Sanskrit. |
| Approach: | They propose a web-based annotation framework, SHR++, for morpho-syntactic annotation of corpora in Sanskrit. |
| Outcome: | The proposed framework reduces the time spent on the annotation tasks by 20.15 %. |
KOTONOHA: A Corpus Concordance System for Skewer-Searching NINJAL Corpora (2020.lrec-1)
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Teruaki Oka, Yuichi Ishimoto, Yutaka Yagi, Takenori Nakamura, Masayuki Asahara, Kikuo Maekawa, Toshinobu Ogiso, Hanae Koiso, Kumiko Sakoda, Nobuko Kibe
| Challenge: | NINJAL has developed several types of corpora for linguistic research . for each corpus NINJAL provided an online search environment, ‘Chunagon’ . |
| Approach: | NINJAL has developed several types of corpora for linguistic research . for each corpus NINJAL provided an online search environment, ‘Chunagon’, which is a morphological-information-annotation-based concordance system made publicly available in 2011 . NINjal has now provided a system ‘Kotonoha’ based on the ‘Chunegon’ systems . |
| Outcome: | NINJAL has provided a skewer-search system ‘Kotonoha’ based on ‘Chunagon’ systems. |
Gamification Platform for Collecting Task-oriented Dialogue Data (2020.lrec-1)
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| Challenge: | a crowd-sourced approach to gather dialogue data is still a challenge due to the complexity of human dialogue structure and diversity of dialogue topics. |
| Approach: | They propose a platform for collecting task-oriented situated dialogue data by using gamification. |
| Outcome: | The proposed platform collects task-oriented situated dialogue data by using gamification. |
Improving the Production Efficiency and Well-formedness of Automatically-Generated Multiple-Choice Cloze Vocabulary Questions (2020.lrec-1)
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| Challenge: | Word Quiz Constructor produces multiple-choice cloze items for vocabulary testing . authors evaluate features to see if they improve production quality and well-formedness . |
| Approach: | They evaluate Word Quiz Constructor features to see whether they improve production quality . items are produced faster and stem sentences are shorter in length without degradation . |
| Outcome: | The proposed features improve production quality and well-formedness of quiz items over previous features. |
Improving Sentence Boundary Detection for Spoken Language Transcripts (2020.lrec-1)
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| Challenge: | Using data expansion and transfer learning, we find that data expansion does not always improve results. |
| Approach: | They propose to divide spoken language into sentence-like units using Topological Fields model . they also propose to use data from the same domain to test different ML architectures . |
| Outcome: | The proposed model improves the detection of boundary detection in spoken dialogues compared to a sequence tagging approach. |
MorphAGram, Evaluation and Framework for Unsupervised Morphological Segmentation (2020.lrec-1)
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| Challenge: | Unsupervised morphological segmentation is beneficial for many natural language processing tasks. |
| Approach: | They propose a framework for unsupervised morphological segmentation that uses Adaptor Grammars. |
| Outcome: | The proposed framework achieves state-of-the-art results across languages of different typologies, from fusional to polysynthetic and from high-resource to low-resourced. |
CTAP for Italian: Integrating Components for the Analysis of Italian into a Multilingual Linguistic Complexity Analysis Tool (2020.lrec-1)
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| Challenge: | Linguistic complexity is a core construct in Second Language Acquisition (SLA) research. |
| Approach: | They present an open source linguistic complexity measurement tool for Italian . they compare it to existing tools for English and germany . |
| Outcome: | The proposed tool is the most comprehensive linguistic complexity measurement tool for italian . it can be used to compare italian texts to multiple other languages in one tool . |
Do you Feel Certain about your Annotation? A Web-based Semantic Frame Annotation Tool Considering Annotators’ Concerns and Behaviors (2020.lrec-1)
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| Challenge: | Existing tools for manual annotations are resourceintensive and complex, and experienced annotators and tools specialized for the purpose of the annotation task are required. |
| Approach: | They propose to use a web-based application with a responsive design for modular semantic frame annotation (SFA) the proposed application keeps track of the time and changes during the annotation process and stores the users’ confidence with the current annotation. |
| Outcome: | The proposed system can be used to build a manually annotated corpus and its arguments for task 2 of SemEval 2019 regarding unsupervised lexical frame induction. |
Seq2SeqPy: A Lightweight and Customizable Toolkit for Neural Sequence-to-Sequence Modeling (2020.lrec-1)
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| Challenge: | Neural models have attracted a lot of attention in the past few years due to their complexity and need to be customized to meet specific needs. |
| Approach: | They propose a lightweight toolkit for sequence-to-sequence modeling that prioritizes simplicity and ability to customize the standard architectures easily. |
| Outcome: | The proposed tool performs similarly or even better than a very widely used sequence-to-sequence toolkit. |
Profiling-UD: a Tool for Linguistic Profiling of Texts (2020.lrec-1)
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| Challenge: | Profiling–UD is a text analysis tool that can be used to characterize language variation from different perspectives. |
| Approach: | They introduce Profiling–UD, a text analysis tool inspired to the principles of linguistic profiling that can support language variation research from different perspectives. |
| Outcome: | The proposed tool is specifically designed to be multilingual since it is based on the Universal Dependencies framework. |
EstNLTK 1.6: Remastered Estonian NLP Pipeline (2020.lrec-1)
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| Challenge: | EstNLTK Python library contains a unified programming interface for natural language processing in Estonian. |
| Approach: | They propose to provide a unified programming interface for natural language processing in Estonian while preserving the intuitive interface for novices. |
| Outcome: | The new interface greatly simplifies processing of large text collections. |
A Tree Extension for CoNLL-RDF (2020.lrec-1)
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| Challenge: | CoNLL-RDF provides a bridge for popular oneword-per-line formats . main reasons for their popularity are the simplicity of tables and tab-separated values . |
| Approach: | They propose a technology that provides a bridge between knowledge graphs and natural language processing. |
| Outcome: | The proposed technology provides a bridge for popular one-word-per-line formats . it provides native support for word-level annotations, but not phrase structures or text structure . |
Lemmatising Verbs in Middle English Corpora: The Benefit of Enriching the Penn-Helsinki Parsed Corpus of Middle English 2 (PPCME2), the Parsed Corpus of Middle English Poetry (PCMEP), and A Parsed Linguistic Atlas of Early Middle English (PLAEME) (2020.lrec-1)
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| Challenge: | Using the lemmatisation of three annotated corpora of Middle English, we hypothesize that verbs copied from Old French favoured and produced grammatical changes in ME . instead of using the more traditional and more problematic term 'borrowing' we use Johanson's term . copying allows for the non-identicality of original and copied material. |
| Approach: | They propose to lemmatise the Penn-Helsinki Parsed Corpus of Middle English 2 (PPCME2), the Parsed corpus of middle english poetry (PCMEP) and A Parsed Linguistic Atlas of Early Middle English (PLAEME) they hypothesize that verbs copied from Old French favoured and produced grammatical changes in ME . |
| Outcome: | The proposed method improves accuracy and recall of the annotated corpus of Middle English and the PLAEME. |
CoCo: A Tool for Automatically Assessing Conceptual Complexity of Texts (2020.lrec-1)
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| Challenge: | Traditional text complexity assessment only takes into account lexical and lexiconal complexity. |
| Approach: | They propose a tool for automatic assessment of conceptual text complexity based on the current state-of-the-art unsupervised approach . they compare the current implementation with the state of the art and discuss the influence of the choice of entity linker on the performance of the tool. |
| Outcome: | The proposed tool can be personalized and adapted to the needs of struggling readers. |
PyVallex: A Processing System for Valency Lexicon Data (2020.lrec-1)
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| Challenge: | PyVallex is a Python-based system for presenting, searching, filtering, editing and processing machine-readable lexicon data . the system provides most of the typical functionalities of a Dictionary Writing System (DWS) |
| Approach: | PyVallex is a Python-based system for presenting, searching, editing and processing machine-readable lexicon data. |
| Outcome: | the open source lexicon system is based on a parser, search engine and web-based interface . it provides most of the typical functionalities of a dictionary writing system . |
Editing OntoLex-Lemon in VocBench 3 (2020.lrec-1)
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Manuel Fiorelli, Armando Stellato, Tiziano Lorenzetti, Andrea Turbati, Peter Schmitz, Enrico Francesconi, Najeh Hajlaoui, Brahim Batouche
| Challenge: | OntoLex-Lemon is a collection of RDF vocabularies for specifying the verbalization of ontologies in natural language. |
| Approach: | They propose to extend existing RDF editor to OntoLex-Lemon to provide more direct editing . they propose to use a model that allows for the verbalization of ontologies in natural language . |
| Outcome: | The proposed editor improves the ontology-lexicon interface and improves its flexibility. |
MALT-IT2: A New Resource to Measure Text Difficulty in Light of CEFR Levels for Italian L2 Learning (2020.lrec-1)
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| Challenge: | Existing methods to assess text difficulty in second or foreign language classrooms are subjective . formal and quantitative characteristics of a text have a major role in determining comprehensibility . |
| Approach: | They propose a system that automatically classifies inputted texts according to CEFR levels . they describe the rationale of the project and the corpus and computational system it is based on . |
| Outcome: | The proposed system is able to predict text difficulty in Italian, and it is reliable, the authors say . they also identify the features which most influenced the predictions . |
Fintan - Flexible, Integrated Transformation and Annotation eNgineering (2020.lrec-1)
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| Challenge: | Fintan is a platform for converting heterogeneous linguistic resources to RDF. |
| Approach: | They introduce Fintan for converting heterogeneous linguistic resources to RDF with its modular architecture, workflow management and visualization features. |
| Outcome: | The Fintan platform is designed to transform linguistic resources to graphs and graphs. |
Contemplata, a Free Platform for Constituency Treebank Annotation (2020.lrec-1)
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| Challenge: | Contemplata is dedicated to the annotation of constituency trees. |
| Approach: | They propose to use Contemplata to build treebanks and treebank enrichment with relations between syntactic nodes. |
| Outcome: | The proposed solution is dedicated to the annotation of constituency trees and provides a balanced strategy between automatic parsing and manual revision. |
Interchange Formats for Visualization: LIF and MMIF (2020.lrec-1)
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| Challenge: | In this paper, we discuss the enhanced data visualization capabilities enabled by interoperating computational linguistics and natural language processing (NLP) applications. |
| Approach: | They propose to use interchange formats to enable enhanced data visualization . they propose to combine CL tools with openly available visualization tools . |
| Outcome: | The proposed formats can be used to create visualizations and manipulate annotations in multiple ways. |
Developing NLP Tools with a New Corpus of Learner Spanish (2020.lrec-1)
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Sam Davidson, Aaron Yamada, Paloma Fernandez Mira, Agustina Carando, Claudia H. Sanchez Gutierrez, Kenji Sagae
| Challenge: | Currently, there is little research on the development of effective NLP tools for the L2 classroom. |
| Approach: | They propose to use an annotated corpus of Spanish learner text to analyze developmental patterns and to develop a grammatical error correction system for Spanish learners. |
| Outcome: | The proposed system is based on annotated learner corpus of Spanish learners and includes error annotations and corrected text. |
DeepNLPF: A Framework for Integrating Third Party NLP Tools (2020.lrec-1)
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Francisco Rodrigues, Rinaldo Lima, William Domingues, Robson Fidalgo, Adrian Chifu, Bernard Espinasse, Sébastien Fournier
| Challenge: | Many third-party NLP tools perform distinct NLP subtasks, but integration is difficult . authors present a framework that enables easy integration of third-parties into a pipeline . |
| Approach: | They propose a framework that enables easy integration of third-party NLP tools . it provides an API for complete pipeline customization including definition of input/output formats . |
| Outcome: | The proposed framework reduces runtime processing time compared to executing the same pipeline in a sequential manner. |