Proceedings of the 28th International Conference on Computational Linguistics
Exploring Controllable Text Generation Techniques (2020.coling-main)
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| Challenge: | Neural controllable text generation has a plethora of applications but there is no unifying theme. |
| Approach: | They propose a new schema for the control of attributes in the generation process by classifying it into five modules and providing an analysis on the advantages and disadvantages of these techniques. |
| Outcome: | The proposed frameworks can be used to control the attributes of natural sentences and to modulate the formality and politeness of emails. |
Infusing Sequential Information into Conditional Masked Translation Model with Self-Review Mechanism (2020.coling-main)
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| Challenge: | Existing non-autoregressive models generate target words in parallel, but with a large latency due to the left-to-right dependency. |
| Approach: | They propose to train a conditional masked translation model and refine results within several iterations to remedy a flawed translation by non-autoregressive models. |
| Outcome: | The proposed model outperforms state-of-the-art models by over 1 BLEU while using less training computations. |
Building Hierarchically Disentangled Language Models for Text Generation with Named Entities (2020.coling-main)
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| Challenge: | Named entities pose a unique challenge to traditional methods of language modeling. |
| Approach: | They propose a Hierarchically Disentangled Model for named entities in cooking recipes using a dataset from several publicly available online sources. |
| Outcome: | The proposed model is based on 158,473 cooking recipes from public sources. |
CharBERT: Character-aware Pre-trained Language Model (2020.coling-main)
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| Challenge: | Pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations . but these methods split a word into subword units and make it incomplete and fragile . |
| Approach: | They propose a character-aware pre-trained language model to tackle OOV problems . they construct contextual word embedding for each token from sequential character representations . |
| Outcome: | The proposed model improves on the existing models on multiple NLP benchmarks. |
A Graph Representation of Semi-structured Data for Web Question Answering (2020.coling-main)
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| Challenge: | Existing studies treat semi-structured data as flat documents with pieces of text . semi-structural data is more effective to represent rich relational information . question answering is an important feature in most search engines . |
| Approach: | They propose a graph representation of Web tables and lists based on categorization of components and their relations . they also develop reasoning techniques on the graph model for the question answering task . |
| Outcome: | The proposed graph improves F1 score by 3.90 points over the state-of-the-art baselines on real datasets. |
Catching Attention with Automatic Pull Quote Selection (2020.coling-main)
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| Challenge: | Using a novel task, we advocate automatic pull quote selection to engage readers with thought-provoking articles . pull quotes increase enjoyment and readability, shape reader perceptions, and facilitate learning. |
| Approach: | They propose a task that automatically selects pull quotes from articles with more salient presentation. |
| Outcome: | The proposed task differs from similar tasks such as summarization and clickbait identification by several aspects. |
MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing (2020.coling-main)
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| Challenge: | Named entity typing (NET) is a classification task of assigning an entity mention in the context with given semantic types. |
| Approach: | They propose a memory-augmented FNET model to tackle unseen types in a zero-shot manner. |
| Outcome: | The proposed model outperforms the state-of-the-art models with up to 8% gain in Micro-F1 and Macro-F1. |
Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations (2020.coling-main)
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| Challenge: | Existing methods treat each span token equally important, ignoring significant features. |
| Approach: | They propose a span-based joint extraction framework with attention-based semantic representations that utilizes span-specific and contextual representations. |
| Outcome: | The proposed model outperforms existing models on ACE2005, CoNLL2004 and ADE. |
Hierarchical Chinese Legal event extraction via Pedal Attention Mechanism (2020.coling-main)
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| Challenge: | Existing methods for event extraction cannot express connections between arguments, which are crucial in legal events. |
| Approach: | They propose a dynamic event structure for Chinese legal events to distinguish between similar events by hierarchical event features for event detection and a pedal attention mechanism to extract the semantic relation between two words through their dependent adjacent words. |
| Outcome: | The proposed model surpasses state-of-the-art models on a Chinese legal event dataset. |
Is Killed More Significant than Fled? A Contextual Model for Salient Event Detection (2020.coling-main)
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| Challenge: | Existing work on identifying the salient information in a text has used a limited representation of events that omits essential information. |
| Approach: | They propose a highly contextual model of event salience that uses a rich representation of events and integrates document-level information. |
| Outcome: | The proposed model improves on an event salience dataset by 2-4% on standard metrics and addresses flaws in existing evaluation methodologies. |
Appraisal Theories for Emotion Classification in Text (2020.coling-main)
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| Challenge: | Automatic emotion categorization is based on textual units assigned to an emotion from a predefined inventory, for instance following the basic emotion classes proposed by Paul Ekman (1999) or Plutchik (2001). |
| Approach: | They propose to make automatic emotion categorization explicit by following theories of cognitive appraisal of events and show their potential for emotion classification when being encoded in classification models. |
| Outcome: | The proposed models improve the classification of discrete emotion categories by using appraisal dimension assignments in event descriptions. |
A Symmetric Local Search Network for Emotion-Cause Pair Extraction (2020.coling-main)
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| Challenge: | Existing methods for Emotion-cause pair extraction are not effective because of their lack of annotation. |
| Approach: | They propose a new task which aims at extracting the potential clause pairs of emotions and corresponding causes in a document. |
| Outcome: | The proposed method outperforms existing state-of-the-art methods on the ECPE corpus. |
Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis (2020.coling-main)
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| Challenge: | Existing methods for aspect sentiment analysis do not include explicit sentiment expressions. |
| Approach: | They propose to construct a heterogeneous graph by leveraging aspect-focused and inter-aspect contextual dependencies for the specific aspect. |
| Outcome: | The proposed model outperforms state-of-the-art methods on four benchmark datasets and significantly boosts performance in comparison with BERT. |
METNet: A Mutual Enhanced Transformation Network for Aspect-based Sentiment Analysis (2020.coling-main)
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| Challenge: | Existing methods for learning complex sentences with multiple aspects are ill-equipped to learn complex sentences . |
| Approach: | They propose a mutual enhanced transformation network for the ABSA task . it improves representation learning of the aspect with contextual semantic features . |
| Outcome: | The proposed model improves representation learning of the aspect with contextual semantic features, giving the aspect more abundant information. |
Making the Best Use of Review Summary for Sentiment Analysis (2020.coling-main)
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| Challenge: | Existing methods for sentiment analysis of user reviews are limited to a few examples. |
| Approach: | They propose a hierarchically-refined attention model that exploits the sentimental distribution of a review and its corresponding summary. |
| Outcome: | The proposed model can make better use of user-written summaries for review sentiment analysis and is more effective compared to existing methods when the user summary is replaced with summary generated by an automatic summarization system. |
From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation (2020.coling-main)
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| Challenge: | Existing sentiment analysis models lack temporal information to capture semantics of long texts. |
| Approach: | They propose a framework to exploit task-related discourse structures for sentiment analysis. |
| Outcome: | The proposed framework improves the performance even beyond existing approaches based on human annotated data. |
End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network (2020.coling-main)
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| Challenge: | Emotion-cause pair extraction (ECPE) aims to extract emotion expressions and their corresponding causes in a document simultaneously. |
| Approach: | They propose to model pair-level contexts so that to capture dependency information among local neighborhood candidate pairs. |
| Outcome: | The proposed model extracts emotion-cause pairs and their causes from documents . it is based on a benchmark Chinese emotion-case pair extraction corpus . |
A Unified Sequence Labeling Model for Emotion Cause Pair Extraction (2020.coling-main)
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| Challenge: | Existing methods for emotion-cause pair extraction cannot distinguish emotion-caused pairs from each other . Existing approaches may suffer from possible cascading errors . |
| Approach: | They propose to assign emotion type labels to emotion and cause clauses so that they can be easily distinguished. |
| Outcome: | The proposed method can extract multiple emotion-cause pairs in an end-to-end fashion. |
Regrexit or not Regrexit: Aspect-based Sentiment Analysis in Polarized Contexts (2020.coling-main)
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| Challenge: | Aspect-based Sentiment Analysis (ABSA) aims at capturing sentiment expressed toward each aspect of a target entity. |
| Approach: | They propose to extend the task of Aspect-based Sentiment Analysis (ABSA) toward affect and emotion representation in polarized settings. |
| Outcome: | The proposed model captures aspect-based polarization from newspapers regarding the Brexit scenario of 1.2m entities at sentence-level. |
Affective and Contextual Embedding for Sarcasm Detection (2020.coling-main)
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| Challenge: | Existing methods to detect sarcasm from text lack vocal intonation or facial gestures in textual data. |
| Approach: | They propose two deep neural network models for sarcasm detection that extend the architecture of BERT by incorporating both affective and contextual features. |
| Outcome: | The proposed models outperform state-of-the-art models on different datasets with significant margins. |
Understanding Pre-trained BERT for Aspect-based Sentiment Analysis (2020.coling-main)
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| Challenge: | Recent studies show impressive results on aspects-based sentiment analysis tasks. |
| Approach: | They analyze the attentions and learned representations of BERT for aspects-based sentiment analysis tasks. |
| Outcome: | The proposed model can be used for aspects-based sentiment analysis (ABSA) but it is not clear how it can provide important features for downstream tasks. |
Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis (2020.coling-main)
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| Challenge: | Cross-domain sentiment analysis is a hot topic in research and industry . domain-invariant representation learning (DIRL) is used to learn a feature representation across domains . but, when label distribution P(Y) shifts across domain, it degrades performance . |
| Approach: | They propose a domain-invariant representation learning framework to improve cross-domain sentiment analysis performance. |
| Outcome: | The proposed model is easy to transfer existing models to the proposed model. |
Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation (2020.coling-main)
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| Challenge: | Existing models for sentiment analysis over tweets require a substantial amount of text to adapt to a domain where the syntax is different. |
| Approach: | They propose to use a multilingual transformer model to train over tweets in five different languages to adapt the model to non-English languages. |
| Outcome: | The proposed model improves over small corpora of tweets in non-English languages. |
Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks (2020.coling-main)
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| Challenge: | End-to-end aspect-based sentiment analysis uses two sub-tasks to extract aspect terms . experimental results demonstrate the effectiveness of our approach on all datasets . |
| Approach: | They propose to combine aspect extraction and sentiment analysis with encoding syntactic information to improve model's representation of input sentences. |
| Outcome: | The proposed approach achieves state-of-the-art on three benchmark datasets. |
Train Once, and Decode As You Like (2020.coling-main)
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| Challenge: | Existing approaches to machine translation support autoregressive, semi-autoregressive and refinement-based non-auto-regressives. |
| Approach: | They propose a unified approach for supporting different generation manners of machine translation including autoregressive, semi-autoregressive and refinement-based non-auto-regressives. |
| Outcome: | The proposed approach achieves better or competitive translation performance compared with strong baseline models in all the settings. |
A Representation Learning Approach to Animal Biodiversity Conservation (2020.coling-main)
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| Challenge: | Using vector embeddings, we can predict the conservation status of animal species using a machine learning model. |
| Approach: | They propose a method for predicting the conservation status of animal species using custom generated name embeddings from Wikipedia text and animal taxonomy data. |
| Outcome: | The proposed model predicts the conservation status of data deficient species and steers more focus towards them for protection. |
Integrating External Event Knowledge for Script Learning (2020.coling-main)
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| Challenge: | Recent studies focus on event co-occurrence to solve this problem. |
| Approach: | They propose to integrate external event knowledge to help predict the next event . they use a script-based approach that integrates external event information into the model . |
| Outcome: | The proposed method achieves state-of-the-art performance compared to other methods. |
Pointing to Subwords for Generating Function Names in Source Code (2020.coling-main)
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| Challenge: | Existing methods for generating function names from source code face difficulties in generating low-frequency or out-of-vocabulary subwords. |
| Approach: | They propose two strategies for copying low-frequency or out-of-vocabulary subwords in inputs. |
| Outcome: | The proposed method improves on the Java-small and Java-large datasets and improves the existing method on the GitHub platform. |
Heterogeneous Graph Neural Networks to Predict What Happen Next (2020.coling-main)
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| Challenge: | Existing work on event representation cannot capture discontinuous event segments . Existing models cannot represent heterogeneous relations and discontinuous events . |
| Approach: | They propose a heterogeneous-event graph network to model missing events . they employ each unique word and individual event as nodes in the graph . |
| Outcome: | The proposed model outperforms baseline models on one-step and multi-step inference tasks. |
CEREC: A Corpus for Entity Resolution in Email Conversations (2020.coling-main)
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| Challenge: | e-mail corpus for entity resolution in email conversations is first large scale annotated corpus . ecc is a two-step process with minimal manual effort. |
| Approach: | They present the first large scale corpus for entity resolution in email conversations . they use 6001 email threads and 38,996 entity coreference chains to construct the corpus . |
| Outcome: | The proposed corpus is the first large scale annotated corpus for entity resolution in email conversations. |
SQL Generation via Machine Reading Comprehension (2020.coling-main)
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| Challenge: | Text-to-SQL systems can generate SQL queries given natural language questions. |
| Approach: | They propose a method that formulates a question answering problem as a query answering problem where different slots are predicted by a unified machine reading comprehension (MRC) model. |
| Outcome: | The proposed method can achieve competitive results on WikiSQL, suggesting it being a promising direction for text-to-SQl. |
Towards Privacy by Design in Learner Corpora Research: A Case of On-the-fly Pseudonymization of Swedish Learner Essays (2020.coling-main)
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| Challenge: | An ongoing project aims at automating pseudonymization of learner essays . 89% of the personal information can be successfully identified in learner data . |
| Approach: | They propose to use rule-based methods to detect 15 categories out of 19 suggested by the authors. |
| Outcome: | The proposed methods detect 15 categories out of 19 suggested by the authors . 89% of the personal information can be successfully identified in learner data and annotated correctly with an inter-annotator agreement of 86% measured as Fleiss kappa and Krippendorff’s alpha. |
PG-GSQL: Pointer-Generator Network with Guide Decoding for Cross-Domain Context-Dependent Text-to-SQL Generation (2020.coling-main)
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| Challenge: | Existing approaches to text-to-SQL generation depend on interaction history and current utterances. |
| Approach: | They propose an encoder-decoder model based on interaction-level encoder to capture historical information of SQL query and reuse the previous SQL query tokens. |
| Outcome: | The proposed model outperforms the previous state-of-the-art model on the SParC benchmark . it achieves 34.0% question matching accuracy and 19.0% interaction matching accuracy . |
Neural Approaches for Natural Language Interfaces to Databases: A Survey (2020.coling-main)
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Radu Cristian Alexandru Iacob, Florin Brad, Elena-Simona Apostol, Ciprian-Octavian Truică, Ionel Alexandru Hosu, Traian Rebedea
| Challenge: | Interest in NLIDBs has resurged in the past years due to the availability of large datasets and improvements to neural sequence-to-sequence models. |
| Approach: | They focus on key design decisions behind current state of the art neural approaches . they highlight linking question tokens to database schema elements . |
| Outcome: | The proposed approaches are grouped into encoder and decoder improvements . they include better architectures for encoding the textual query taking into account the schema and improved generation of structured queries using autoregressive neural models. |
Predicting Stance Change Using Modular Architectures (2020.coling-main)
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| Challenge: | Existing studies on argumentative text in isolation have shown that ideological stances are highly correlated with different moral arguments preferences. |
| Approach: | They propose a modular learning approach which decomposes the task into multiple modules and focuses on different aspects of the interaction between users, their beliefs, and the arguments they are exposed to. |
| Outcome: | The proposed approach archives significantly better results over the end-to-end approach using BERT over the same inputs. |
Leveraging HTML in Free Text Web Named Entity Recognition (2020.coling-main)
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| Challenge: | Named Entity Recognition (NER) is the identification of the proper names of objects. |
| Approach: | They compare HTML tags discarded in free text Named Entity Recognition from Web pages . they find an increased F1 performance for Text+Tags of between 0.9% and 13.2% . |
| Outcome: | The proposed method improves F1 performance over datasets, variants and models. |
Multimodal Review Generation with Privacy and Fairness Awareness (2020.coling-main)
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| Challenge: | Existing frameworks for generating personalized reviews take privacy and fairness into account . users generate digital footprints when "traveling" on the internet . |
| Approach: | They propose a neural-based framework that generates personalized reviews with privacy and fairness in mind. |
| Outcome: | The proposed framework generates plausibly long reviews while controlling the amount of exploited user data and using the least sentiment biased embeddings. |
Generating Equation by Utilizing Operators : GEO model (2020.coling-main)
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| Challenge: | Existing neural models that use hand-crafted features are expensive and lack domain-specific knowledge. |
| Approach: | They propose a GEO model that uses operator-based features to generate equations using natural language sentences. |
| Outcome: | The proposed model outperforms state-of-the-art models on two datasets and 82.1% in ALG514. |
Improving Abstractive Dialogue Summarization with Graph Structures and Topic Words (2020.coling-main)
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| Challenge: | Existing approaches to summarize textual information are hard to capture long-distance relationships. |
| Approach: | They propose a Topic-word Guided Dialogue Graph Attention network to model the dialogue as an interaction graph according to topic word information. |
| Outcome: | The proposed model outperforms baseline models on two corpus corpus models. |
Speaker-change Aware CRF for Dialogue Act Classification (2020.coling-main)
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| Challenge: | Recent work in Dialogue Act (DA) classification approaches the task as a sequence labeling problem, using neural network models coupled with a Conditional Random Field (CRF) as the last layer. |
| Approach: | They propose to modify the CRF layer to take speaker-change into account and learn meaningful transition patterns conditioned on speaker-changing DA labels. |
| Outcome: | The proposed model outperforms the original model with wide margins for some DA labels. |
LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization (2020.coling-main)
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Nurul Lubis, Christian Geishauser, Michael Heck, Hsien-chin Lin, Marco Moresi, Carel van Niekerk, Milica Gasic
| Challenge: | Reinforcement learning (RL) can be used to steer a conversation towards successful task completion. |
| Approach: | They propose to use latent latent variables to shape latent variable distributions . they use response auto-encoding as auxiliary task to capture generative factors . |
| Outcome: | The proposed approach yields a more action-characterized latent representations . the proposed approach achieves state-of-the-art success rates . |
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey (2020.coling-main)
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| Challenge: | In recent years, neural-network based models have been used for a wide range of tasks, including slot filling and intent classification. |
| Approach: | They propose three neural architectures to model slot filling and intent classification . they propose independent models, joint models and transfer learning models that exploit the mutual benefit of the two tasks simultaneously and scale the model to new domains. |
| Outcome: | The proposed models model SF and IC separately, exploit mutual benefit of the two tasks simultaneously and scale the model to new domains. |
Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task Learning (2020.coling-main)
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| Challenge: | Using a multi-task learning framework, we train a universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging and utterance segmentation in a simple deep recurrent setting. |
| Approach: | They propose a multi-task learning framework to train a universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging and utterance segmentation in a simple deep recurrent setting. |
| Outcome: | The proposed model outperforms individual tasks and delivers competitive performance. |
AprilE: Attention with Pseudo Residual Connection for Knowledge Graph Embedding (2020.coling-main)
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| Challenge: | Existing knowledge graph embedding methods are difficult to model diverse relational patterns, especially symmetric and antisymmetric relations. |
| Approach: | They propose a model which employs triple-level self-attention and pseudo residual connection to model relational patterns. |
| Outcome: | The proposed model significantly outperforms state-of-the-art models on public datasets on symmetric and antisymmetric relations. |
Variational Autoencoder with Embedded Student-t Mixture Model for Authorship Attribution (2020.coling-main)
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| Challenge: | Existing Variational autoencoders are limited by the assumed Gaussianity of the underlying probability distributions in the latent space. |
| Approach: | They propose a probabilistic autoencoding framework to deal with a supervised authorship attribution task. |
| Outcome: | The proposed method outperforms existing methods on an Amazon review dataset. |
Knowledge Graph Embeddings in Geometric Algebras (2020.coling-main)
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| Challenge: | Existing knowledge graph embedding approaches model entities and relations in KGs using real-valued, complex-value, or hypercomplex-value representations. |
| Approach: | They propose a geometric algebra-based KG embedding framework which uses multivector representations and the geometric product to model entities and relations. |
| Outcome: | The proposed framework outperforms state-of-the-art models for link prediction. |
Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial Regularization (2020.coling-main)
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Qiuhao Lu, Nisansa de Silva, Dejing Dou, Thien Huu Nguyen, Prithviraj Sen, Berthold Reinwald, Yunyao Li
| Challenge: | Existing graph autoencoders and its variants have been used for node embedding . a new method is proposed to model consistency across different views of networks . |
| Approach: | They propose a network embedding method which enforces latent representations to be consistent across different views of networks by incorporating a multiview adversarial regularization module. |
| Outcome: | The proposed method compares favorably with the state-of-the-art methods on benchmark datasets and on a real-world application. |
RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion (2020.coling-main)
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| Challenge: | Existing approaches for knowledge graph embedding have limitations in complex vector space . embeddability of one-to-many relations is not explicitly alleviated . |
| Approach: | They propose a relation-adaptive translating embedding function that can be extended to complex vector space. |
| Outcome: | The proposed translation function improves expressive power and alleviates embedding ambiguity problem. |
SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis (2020.coling-main)
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| Challenge: | Pre-trained language models have been widely applied to cross-domain NLP tasks like sentiment analysis, but fine-tuning them on the source domain tends to overfit, leading to inferior results on the target domain. |
| Approach: | They propose to pre-train a sentiment-aware language model (SentiX) via domain-invariant sentiment knowledge from large-scale review datasets and utilize it for cross-domain sentiment analysis tasks without fine-tuning. |
| Outcome: | The proposed model achieves state-of-the-art in all the cross-domain sentiment analysis tasks and can be trained with only 1% samples and better than BERT with 90% samples. |
Bayes-enhanced Lifelong Attention Networks for Sentiment Classification (2020.coling-main)
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| Challenge: | Existing deep learning paradigms focus on learning a model from training data of a single task and the learned model is also tested on the same task. |
| Approach: | They propose a Bayes-enhanced lifelong attention network to learn attention knowledge from a sequence of sentiment classification tasks and build lifelong ones. |
| Outcome: | The proposed model is able to learn attention knowledge from a set of sentiment classification tasks and build lifelong attentions. |
Arabizi Language Models for Sentiment Analysis (2020.coling-main)
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| Challenge: | Arabizi is a written form of spoken Arabic, relying on Latin characters and digits. |
| Approach: | They propose to use Arabizi as a written form of spoken Arabic in online social networks . they use a corpus of 7.7M tweets written in Arabizi and a subset of SALAD to train a model in Arabic . |
| Outcome: | The proposed model outperforms state-of-the-art models on sentiment analysis task using arabizi . the proposed model is based on a corpus of 7.7M tweets written in arabizi and a subset of LAD manually annotated for sentiment analysis. |
Author’s Sentiment Prediction (2020.coling-main)
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| Challenge: | Existing work on inferring author sentiment in news articles hasn't been done on this domain. |
| Approach: | They propose a crowd-sourced dataset that captures the sentiment of an author towards the main entity in a news article. |
| Outcome: | The proposed dataset performs the best amongst the baselines, but only achieves modest performance overall suggesting that fine-tuning document-level representations aloneisn’t adequate for this task. |
Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions (2020.coling-main)
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| Challenge: | a novel context-aware dynamic convolution network is proposed to better leverage the local contexts when dynamically generating convolution kernels. |
| Approach: | They propose a dynamic convolution network to leverage local contexts when generating convolution kernels. |
| Outcome: | The proposed frameworks achieve state-of-the-art on two benchmark datasets. |
Named Entity Recognition for Chinese biomedical patents (2020.coling-main)
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| Challenge: | Existing attempts to address NER for Chinese biomedical texts have been limited due to the amount of Chinese biomedicine discoveries being patented. |
| Approach: | They train and evaluate Chinese biomedical patents NER models based on BERT . their model is optimized for Chinese bio-patent data and scored an F1 . |
| Outcome: | The proposed model achieves an F1 score of 0.540.15 for Chinese biomedical patent data. |
Learning Health-Bots from Training Data that was Automatically Created using Paraphrase Detection and Expert Knowledge (2020.coling-main)
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| Challenge: | Current dialog models require large quantities of training data due to privacy issues . current methods require large amounts of training and are even scarcer in the health domain . |
| Approach: | They propose a method for creating dialog corpora from physician-patient interaction data . they combine expert knowledge provided by physicians with automatic paraphrase extraction techniques . |
| Outcome: | The proposed method outperforms existing models in the health domain . it provides a natural way of guiding learning and improves quality of human-machine interactions . |
A Joint Learning Approach based on Self-Distillation for Keyphrase Extraction from Scientific Documents (2020.coling-main)
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| Challenge: | Existing methods for keyphrase extraction are limited by the number of annotated documents. |
| Approach: | They propose a joint learning approach that uses the idea of self-distillation to extract keyphrases from unlabeled articles. |
| Outcome: | The proposed approach outperforms baseline models on two public benchmarks: Inspec and SemEval-2017. |
Enhancing Clinical BERT Embedding using a Biomedical Knowledge Base (2020.coling-main)
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| Challenge: | Domain knowledge is important for building Natural Language Processing (NLP) systems for low-resource settings, such as in the clinical domain. |
| Approach: | They propose a joint method for adding knowledge base information from the Unified Medical Language System (UMLS) into language model pre-training for some clinical domain corpus. |
| Outcome: | The proposed method outperforms existing models on three clinical domain tasks with no knowledge base information. |
TIMBERT: Toponym Identifier For The Medical Domain Based on BERT (2020.coling-main)
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| Challenge: | Phylogeographers have long linked the geographical spread of viruses to the growth in global tourism and international trade of goods. |
| Approach: | They propose to automate the process of place name detection in the medical domain by learning the mapping from an input sentence to the associated sentence labeled with toponyms. |
| Outcome: | The proposed model achieves an F1 score of 90.85%, compared to the state-of-the-art of 89.13%. |
BioMedBERT: A Pre-trained Biomedical Language Model for QA and IR (2020.coling-main)
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| Challenge: | SARS-CoV-2 pandemic highlighted importance of moving quickly with biomedical research. |
| Approach: | They propose a textual data mining tool that supports literature search to accelerate the work of researchers in the biomedical domain. |
| Outcome: | The proposed model achieves state-of-the-art results on the QA fine-tuning task on BioASQ 5b, 6b and 7b datasets. |
Extracting Adherence Information from Electronic Health Records (2020.coling-main)
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Jordan Sanders, Meghana Gudala, Kathleen Hamilton, Nishtha Prasad, Jordan Stovall, Eduardo Blanco, Jane E Hamilton, Kirk Roberts
| Challenge: | adherence is a critical factor in health outcomes, and is often modeled as a binary decision . adherence models include intentional and unintentional non-adherence, social support and other patient attributes such as age and time since diagnosis. |
| Approach: | They propose to extract adherence information from electronic health records using de-identified sentences and a corpus of 3,000 de-identified sentences. |
| Outcome: | The proposed framework extracts medication adherence information from electronic health records. |
Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework (2020.coling-main)
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Shweta Yadav, Jainish Chauhan, Joy Prakash Sain, Krishnaprasad Thirunarayan, Amit Sheth, Jeremiah Schumm
| Challenge: | Existing studies on social media for deriving mental health status of users focus on the depression detection task. |
| Approach: | They propose to use a BERT based robust multi-task learning framework to accurately identify the depressive symptoms using the auxiliary task of figurative usage detection. |
| Outcome: | The proposed model improves its robustness and reliability for distinguishing the depression symptoms. |
French Biomedical Text Simplification: When Small and Precise Helps (2020.coling-main)
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| Challenge: | Existing studies on text simplification in English use large parallel monolingual corpora in which one complex sentence is paired with one or more simplified versions. |
| Approach: | They use parallel sentences from existing health comparable corpora in French and WikiLarge corpus translated from English to French and a lexicon that associates medical terms with paraphrases. |
| Outcome: | The proposed models are based on sentences from existing health comparable corpora in French and WikiLarge corpus translated from English to French. |
Summarizing Medical Conversations via Identifying Important Utterances (2020.coling-main)
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| Challenge: | Applying natural language processing (NLP) techniques to the medical field is a prevailing trend nowadays and has great potential in many applications, such as key information extraction in medical literature. |
| Approach: | They propose to use a hierarchical encoder-tagger model to generate medical conversation summarization by identifying important utterances. |
| Outcome: | The proposed model outperforms baseline models and models and adds conversation-related features to improve performance. |
Probing Multimodal Embeddings for Linguistic Properties: the Visual-Semantic Case (2020.coling-main)
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| Challenge: | Semantic embeddings have advanced the state of the art for natural language processing tasks . but their inner workings are poorly understood and there is a shortage of analysis tools . |
| Approach: | They propose to extend visual-semantic embeddings to multimodal domains by defining probing tasks for embeddable image-caption pairs and testing them with classifiers. |
| Outcome: | The proposed probing tasks show up to 16% more accurate on visual-semantic embeddings compared to unimodal embedders . the proposed extensions to multimodal domains have been lauded as promising in natural language processing . |
Linguistic Profiling of a Neural Language Model (2020.coling-main)
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| Challenge: | Neural Language Models (NLMs) have become a central component in NLP systems over the last few years, showing outstanding performance and improving the state-of-the-art on many tasks. |
| Approach: | They use a wide set of probing tasks to investigate how a Neural Language Model learns linguistic properties before and after a fine-tuning process. |
| Outcome: | The proposed model can encode a wide range of linguistic characteristics but loses this information when trained on specific downstream tasks. |
IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP (2020.coling-main)
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| Challenge: | despite being spoken by 200 million people, the Indonesian language is underrepresented in NLP research. |
| Approach: | They propose a dataset for Indonesian that includes seven NLP tasks . they also propose 'indonesian language evaluation Montage' tasks that are based on previous work . |
| Outcome: | The proposed dataset shows that IndoBERT outperforms IndoLEM over most of the tasks. |
A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English (2020.coling-main)
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Marius Mosbach, Stefania Degaetano-Ortlieb, Marie-Pauline Krielke, Badr M. Abdullah, Dietrich Klakow
| Challenge: | Despite the high performance of transformer-based language models, we still lack understanding of the kind of linguistic knowledge they learn and rely on. |
| Approach: | They evaluate three transformer-based language models and test their grammatical and semantic knowledge by sentence-level probing, diagnostic cases, and masked prediction tasks. |
| Outcome: | The models capture grammatical and semantic knowledge, but they lack model-specific weaknesses especially on semantic knowledge. |
Modeling language evolution and feature dynamics in a realistic geographic environment (2020.coling-main)
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| Challenge: | a number of studies have examined the stability or biases of typological features within language families . |
| Approach: | They propose a model for simulating languages and their features over time in a realistic geographic environment. |
| Outcome: | The proposed model is flexible and realistic, and can be used to answer questions. |
Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification (2020.coling-main)
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| Challenge: | Existing approaches to aspect-level sentiment classification focus on modeling the relationship between aspect words and their contexts with attention, and ignore the use of elaborate knowledge implicit in the context. |
| Approach: | They exploit syntactic awareness to the model by the graph attention network on the dependency tree structure and external pre-training knowledge by BERT language model, which helps to model the interaction between the context and aspect words better. |
| Outcome: | The proposed model can model the interaction between the context and aspect words better by using syntactic awareness and external pre-training knowledge. |
Attention Transfer Network for Aspect-level Sentiment Classification (2020.coling-main)
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| Challenge: | Aspect-level sentiment classification aims to detect the sentiment polarity of a given opinion target in a sentence. |
| Approach: | They propose a novel attention transfer network which can exploit attention from document-level sentiment datasets to improve the attention capability of the aspect-level classification task. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two ASC benchmark datasets. |
Label Correction Model for Aspect-based Sentiment Analysis (2020.coling-main)
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| Challenge: | Existing models for aspect-based sentiment analysis ignore a phenomenon: aspect boundary label and sentiment label can correct each other. |
| Approach: | They propose a model that uses aspect boundary label and sentiment label to correct each other . they evaluate the model on three benchmark datasets and evaluate its performance . |
| Outcome: | The proposed model performs state-of-the-art on three benchmark datasets. |
Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network (2020.coling-main)
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| Challenge: | Aspect-based sentiment analysis studies focus on identifying sentiment polarities toward explicit aspects but ignore implicit aspects in text. |
| Approach: | They propose a hierarchy-sentiment hierarchy prediction problem to capture explicit and implicit aspects of aspect-based sentiment analysis. |
| Outcome: | The proposed model can consistently achieve the best results on four benchmarks. |
Constituency Lattice Encoding for Aspect Term Extraction (2020.coling-main)
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| Challenge: | a challenge for aspect term extraction is to extract phrase-level aspect terms . a constituency lattice structure is constructed using the span annotations of constituents of a sentence . |
| Approach: | They propose to incorporate the span annotations of constituents of a sentence to leverage syntactic information in neural network models. |
| Outcome: | The proposed model outperforms existing models on two benchmark datasets. |
A Corpus for Argumentative Writing Support in German (2020.coling-main)
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| Challenge: | In today's world most information is readily available. Consequently, the sole reproduction of information is losing attention. |
| Approach: | They propose an annotation approach to capture claims and premises of arguments and their relations in student-written peer reviews on business models in german language. |
| Outcome: | The proposed annotation scheme guides annotators to moderate agreement with the proposed scheme on 50 persuasive student-written peer reviews on business models. |
Do Word Embeddings Capture Spelling Variation? (2020.coling-main)
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| Challenge: | Using word embeddings, we analyze spelling variation in word embeds trained on Twitter and Reddit data. |
| Approach: | They propose a new perspective on the analysis of word embeddings by focusing on spelling variation. |
| Outcome: | The proposed analysis shows that word embeddings encode spelling variation patterns of various types to some extent, even when trained using the skipgram model. |
Don’t take “nswvtnvakgxpm” for an answer –The surprising vulnerability of automatic content scoring systems to adversarial input (2020.coling-main)
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| Challenge: | Automated content scoring systems can be used on short answer tasks to save human effort, but can invite cheating strategies such as writing irrelevant answers. |
| Approach: | They generate adversarial answers for benchmark content scoring datasets based on different methods of increasing sophistication and examine countermeasures such as adversarials. |
| Outcome: | The proposed methods show that even simple methods can reduce content scoring performance but do not solve the problem. |
Automated Prediction of Examinee Proficiency from Short-Answer Questions (2020.coling-main)
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| Challenge: | Existing approaches to predicting examinee proficiency from short-answer questions (SAQs) use of labeled data to train on is difficult, and requires expensive expert-rated data. |
| Approach: | They propose a method to predict examinee proficiency from short-answer questions . previous approaches train on manually labeled data to predict human-ratings assigned to SAQs . |
| Outcome: | The proposed model examines examinee proficiency directly and does not require manual training on labeled data. |
Exploring Cross-sentence Contexts for Named Entity Recognition with BERT (2020.coling-main)
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| Challenge: | Named entity recognition (NER) is often addressed as a sequence classification task with each input consisting of one sentence of text. |
| Approach: | They propose a method to combine different predictions from multiple sentences in input samples to increase NER performance. |
| Outcome: | The proposed method improves on the state-of-the-art NER results on English, Dutch, and Finnish and achieves the best reported BERT-based results on German. |
Cross-lingual Annotation Projection in Legal Texts (2020.coling-main)
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| Challenge: | a new study examines annotation projection in text classification problems where source documents are published in multiple languages. |
| Approach: | They propose to use word embeddings and dynamic time warping to create an annotation corpus for text classification problems where source documents are published in multiple languages. |
| Outcome: | The proposed method is based on word embeddings and dynamic time warping . the aim is to train linguistic tools for the target language without experts . |
Deep Learning Framework for Measuring the Digital Strategy of Companies from Earnings Calls (2020.coling-main)
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| Challenge: | Despite efforts to adopt digital technologies, the success rate in improving business performance is very low due to the lack of a coherent digital strategy. |
| Approach: | They apply NLP models to earnings calls to understand different clusters of digital strategy patterns that companies are Adopting. |
| Outcome: | The proposed models show that Fortune 500 companies use four distinct strategies which are product-led, customer experience-led and service-led. |
A Dataset and Evaluation Framework for Complex Geographical Description Parsing (2020.coling-main)
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| Challenge: | Previously, work on toponym resolution has focused on identifying and resolving individual toponyms in text like Adrano, S.Maria di Licodia or Catania. |
| Approach: | They propose a method that parses a set of coordinates and a collection of 360,187 uncurated complex geolocation descriptions to automate the process. |
| Outcome: | The proposed approach automates most of the process by combining Wikipedia and OpenStreetMap. |
DocBank: A Benchmark Dataset for Document Layout Analysis (2020.coling-main)
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| Challenge: | Existing approaches for document layout analysis are based on rule-based or machine learning methods that ignore textual information. |
| Approach: | They present a benchmark document layout analysis dataset using a computer vision model . they build strong baselines and manually split train/dev/test sets for evaluation . |
| Outcome: | The proposed model trains on DocBank accurately recognize layout information for a variety of documents. |
Building Large-Scale English and Korean Datasets for Aspect-Level Sentiment Analysis in Automotive Domain (2020.coling-main)
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| Challenge: | Existing datasets in automotive domain cover only three languages due to high cost of human annotation. |
| Approach: | They build large-scale datasets of users’ comments in two languages, English and Korean, for aspect-level sentiment analysis in automotive domain. |
| Outcome: | The datasets consist of 58,000+ commentaspect pairs, which are the largest compared to existing datasets. |
A High Precision Pipeline for Financial Knowledge Graph Construction (2020.coling-main)
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Sarah Elhammadi, Laks V.S. Lakshmanan, Raymond Ng, Michael Simpson, Baoxing Huai, Zhefeng Wang, Lanjun Wang
| Challenge: | Knowledge graphs are a standard for structured knowledge representation in the Semantic Web. |
| Approach: | They propose to extract financial news articles into a knowledge graph by using a financial dictionary. |
| Outcome: | The proposed pipeline extracts 342,000 financial news articles with a precision of 78% at the top-100 extractions. |
Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets (2020.coling-main)
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| Challenge: | Recent dominance of machine learning-based natural language processing methods has overemphasized model accuracies rather than studying the reasons behind their errors. |
| Approach: | They investigate the error patterns of some widely acknowledged sentiment analysis methods in the finance domain. |
| Outcome: | The proposed models are based on the existing models and have important clues for improving them. |
Answering Legal Questions by Learning Neural Attentive Text Representation (2020.coling-main)
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| Challenge: | Existing methods for retrieval-based question answering are limited by legal documents and long and complicated documents. |
| Approach: | They propose a retrieval-based model for answering legal questions at the article level by learning neural attentive text representation. |
| Outcome: | The proposed model outperforms state-of-the-art retrieval-based methods on an annotated corpus of 5,922 Vietnamese legal questions in terms of recall and NDCG. |
Joint Transformer/RNN Architecture for Gesture Typing in Indic Languages (2020.coling-main)
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| Challenge: | Gesture typing is a method of typing words on a touch-based keyboard by drawing a continuous trace passing through the relevant keys. |
| Approach: | They propose a keyboard that supports gesture typing in Indic languages by drawing a continuous trace over the keyboard and the finger needs to be lifted only once a word is completed. |
| Outcome: | The proposed model performs path decoding, transliteration and transliterations correction. |
Automatic Charge Identification from Facts: A Few Sentence-Level Charge Annotations is All You Need (2020.coling-main)
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| Challenge: | Existing work on charge-side representations but not much effort has been made in improving fact-side models. |
| Approach: | They propose to use sentence-level charge labels as an auxiliary task coupled with the main task of document-level charging identification in a multi-task learning framework to improve fact-side representations. |
| Outcome: | The proposed model outperforms a large number of baselines on a document-level charge identification task. |
Context-Aware Text Normalisation for Historical Dialects (2020.coling-main)
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| Challenge: | Context-aware historical text normalisation is a severely under-researched area . a new approach to normalise historical spellings relies on the state-of-the-art methods . |
| Approach: | They propose a multidialect normaliser with a context-aware reranking approach . they incorporate dialectal information into the training and use a word-level n-gram language model . |
| Outcome: | The proposed approach improves accuracy on historical datasets and further improves on baseline. |
RuSemShift: a dataset of historical lexical semantic change in Russian (2020.coling-main)
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| Challenge: | et al. (2017) lexical semantic change analysis is still mostly done for English because of limited resources. |
| Approach: | They present a large-scale manually annotated test set for semantic change modeling in Russian . they use DURel framework to annotate Russian for two long-term time periods . |
| Outcome: | The proposed model performs well, but there are still areas for improvement . the results are promising, but the authors say they need to improve . |
Exploring Amharic Sentiment Analysis from Social Media Texts: Building Annotation Tools and Classification Models (2020.coling-main)
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| Challenge: | Existing crowdsourcing platforms do not support sentiment analysis for Amharic, and there are no expert researchers in the area. |
| Approach: | They propose to build a social-network-friendly Amharic sentiment analysis tool using the Telegram bot and collect 9.4k tweets where each tweet is annotated by three Telegram users. |
| Outcome: | The proposed system outperforms existing classifiers in Amharic and other low-resource languages due to the widespread use of sarcasm and figurative speech. |
Effective Few-Shot Classification with Transfer Learning (2020.coling-main)
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| Challenge: | Recent work on few-shot learning addresses the problem of learning based on a small amount of training data. |
| Approach: | They adapt the Amazon Review Sentiment Classification (ARSC) text dataset for few-shot learning . they train a single binary classifier to learn all few- shot classes jointly . |
| Outcome: | The proposed approach outperforms most published results on the ARSC text dataset . the results suggest that the classes in the AR SC few-shot task are very similar to each other . |
SWAFN: Sentimental Words Aware Fusion Network for Multimodal Sentiment Analysis (2020.coling-main)
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| Challenge: | Existing studies focus on learning the joint representation of multiple modalities, ignoring useful knowledge contained in language modal. |
| Approach: | They propose to incorporate sentimental words knowledge into the fusion network to guide the learning of joint representation of multimodal features. |
| Outcome: | The proposed method improves the fusion representation of multimodal features on a YouTube and video dataset. |
Multimodal Topic-Enriched Auxiliary Learning for Depression Detection (2020.coling-main)
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| Challenge: | Existing studies on depression detection rely on textual and visual content to determine whether a human being is depressed or non-depressed. |
| Approach: | They propose a multimodal topic-enriched Auxiliary Learning approach that captures topic information from texts and images for depression detection. |
| Outcome: | The proposed approach improves the performance of the primary task by using topic information from text and images. |
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games (2020.coling-main)
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Alessandro Suglia, Antonio Vergari, Ioannis Konstas, Yonatan Bisk, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon
| Challenge: | Existing models fail to learn multi-modal representations, relying on category labels at inference time. |
| Approach: | They propose a "imagination" module that learns context-aware and category-awful latent embeddings without relying on category labels at inference time. |
| Outcome: | The imagination module outperforms state-of-the-art competitors by 8.26% gameplay accuracy in the CompGuessWhat?! benchmark. |
Situated and Interactive Multimodal Conversations (2020.coling-main)
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Seungwhan Moon, Satwik Kottur, Paul Crook, Ankita De, Shivani Poddar, Theodore Levin, David Whitney, Daniel Difranco, Ahmad Beirami, Eunjoon Cho, Rajen Subba, Alborz Geramifard
| Challenge: | Situated Interactive MultiModal Conversations (SIMMC) is a new direction for virtual assistants that handle multimodal inputs and perform multimodal actions. |
| Approach: | They propose to use Situated Interactive MultiModal Conversations (SIMMC) to train agents to take multimodal actions grounded in a co-evolving multimodal context. |
| Outcome: | The proposed model will be made publicly available. |
Meet Changes with Constancy: Learning Invariance in Multi-Source Translation (2020.coling-main)
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| Challenge: | Existing approaches to multi-source neural machine translation neglect inconsistencies between sources of information. |
| Approach: | They propose a source invariance network to learn invariant information of parallel sources . they propose to integrate such network with multi-encoder based multi-source NMT methods . |
| Outcome: | The proposed approach achieves clear gains in translation quality and captures implicit invariance between different sources. |
Enhancing Neural Models with Vulnerability via Adversarial Attack (2020.coling-main)
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| Challenge: | Existing work on adversarial attack to improve performance of NLSM tasks has not been done. |
| Approach: | They propose a general two-stage training framework to enhance neural models with Vulnerability via adversarial attack. |
| Outcome: | The proposed framework improves neural models with Vulnerability via adversarial attack on NLSM datasets. |
R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning (2020.coling-main)
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| Challenge: | Concept prerequisite chain learning is an unsupervised task with no access to labeled concept pairs during training. |
| Approach: | They propose a model that uses deep learning representations to predict concept relations . they frame concept prerequisite chain learning as an unsupervised task with no labeled concept pairs . |
| Outcome: | The proposed model outperforms semi-supervised methods in terms of accuracy and F1 score. |
Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning (2020.coling-main)
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| Challenge: | Recent work has explored the suitability of pre-trained language models in low resource settings with less than 1,000 training data points. |
| Approach: | They propose to use pool-based active learning to speed up training while keeping the cost of labeling new data constant. |
| Outcome: | The proposed model can be fine-tuned to optimize for low-resource settings while keeping the cost of labeling constant. |
Exploring End-to-End Differentiable Natural Logic Modeling (2020.coling-main)
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| Challenge: | Existing approaches to integrate natural logic with neural networks are brittle and prone to fail in the presence of noise and uncertainty. |
| Approach: | They propose to integrate natural logic with neural networks to create differentiable models that integrate natural reasoning with subsymbolic vector representations and neural components. |
| Outcome: | The proposed model can model monotonicity-based reasoning, compared to baseline models without inductive bias. |
A Semantically Consistent and Syntactically Variational Encoder-Decoder Framework for Paraphrase Generation (2020.coling-main)
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| Challenge: | Paraphrase generation is a longstanding problem in natural language processing (NLP) Neural network-based methods have shown great progress on paraphrase generation. |
| Approach: | They propose a framework that integrates variational inference on a target-related latent variable to introduce the diversity. |
| Outcome: | The proposed framework outperforms baseline models on the metrics based on n-gram matching and semantic similarity, and it can generate multiple different paraphrases by assembling different syntactic variables. |
Tiny Word Embeddings Using Globally Informed Reconstruction (2020.coling-main)
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| Challenge: | Existing methods for word embedding reconstruction use only local information of subwords and pre-trained word embeds. |
| Approach: | They propose a global loss function that uses words other than the target word to improve word embedding reconstruction by a factor of 200. |
| Outcome: | The proposed method reduces the model size of pre-trained word embeddings by a factor of 200 while preserving its quality. |
Improving Word Embeddings through Iterative Refinement of Word- and Character-level Models (2020.coling-main)
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| Challenge: | Embedding of rare and out-of-vocabulary words is an important open NLP problem . standard embedding models are not useful for recommending jobs to users with rare or unseen words . |
| Approach: | They propose to train a character-level neural network to reproduce word embeddings . they then use the model to assign vectors to any input string, including rare words . |
| Outcome: | The proposed method outperforms existing methods on word similarity data sets and can be applied to job title normalization in the e-recruitment domain. |
Probing Multilingual BERT for Genetic and Typological Signals (2020.coling-main)
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| Challenge: | Recent cross-lingual models provide representations for about 100 languages and vary in their training objectives. |
| Approach: | They probe the layers in multilingual BERT for phylogenetic and geographic language signals across 100 languages and compute language distances based on the mBERT representations. |
| Outcome: | The proposed model is best explained by phylogenetic and worst by structural factors and correlates with published ranked lists based on linguistic approaches. |
Learning Efficient Task-Specific Meta-Embeddings with Word Prisms (2020.coling-main)
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| Challenge: | Word embeddings possess different lexical properties depending on the notion of context defined at training time. |
| Approach: | They introduce a meta-embedding method that learns to combine source embeddings according to the task at hand. |
| Outcome: | The proposed method improves performance on six extrinsic evaluations over other methods. |
Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution (2020.coling-main)
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| Challenge: | Lexical substitution is a powerful technology used in various NLP applications . it generates plausible words that can replace a given word in a textual context . |
| Approach: | They propose to use a large-scale comparative study to compare lexical substitution methods . they compare existing and new methods using word sense induction datasets . |
| Outcome: | The proposed methods improve competitive results by incorporating information about the target word into the models. |
Word Embedding Binarization with Semantic Information Preservation (2020.coling-main)
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| Challenge: | Word and Sentence embeddings are one of the most common starting points of any NLP task. |
| Approach: | They propose a way to convert word embedding to binary representation to reduce overall size . they propose different approaches suitable for different downstream tasks based on contextual and semantic information. |
| Outcome: | The proposed method reduces the size of the embedding while keeping the semantic and relational knowledge intact. |
How Relevant Are Selectional Preferences for Transformer-based Language Models? (2020.coling-main)
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| Challenge: | Selectional preference is defined as the tendency of a predicate to favor particular arguments within a certain linguistic context and reject others that result in conflicting or implausible meanings. |
| Approach: | They examine the probability that Bert assigns a dependent word given the presence of a head word in a sentence to determine whether selectional preference exists. |
| Outcome: | The proposed model is based on the SP-10K corpus of selectional preference and the ukWaC corpus. |
Embedding Semantic Taxonomies (2020.coling-main)
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| Challenge: | Recent work on hierarchical representational structures in machine learning promises to blend the value of human curated taxonomies with the power and flexibility of machine learning systems. |
| Approach: | They propose to use box embeddings to encode aspects of partial ordering property of taxonomies to represent a medical subject headings taxonomy. |
| Outcome: | The proposed model outperforms baselines for taxonomic reconstruction and bipartite relationship experiments and is compared with a set of 300K PubMed articles with subject labels from MeSH. |
A Retrofitting Model for Incorporating Semantic Relations into Word Embeddings (2020.coling-main)
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| Challenge: | Existing word embedding models mix semantic similarity with other types of relatedness. |
| Approach: | They propose a model that leverages relational knowledge available in a knowledge resource to improve word embeddings. |
| Outcome: | The proposed model improves word embeddings on synonymy, antonymy and hypernymy relations in WordNet and significantly improves lexical entailment detection task. |
Lexical Relation Mining in Neural Word Embeddings (2020.coling-main)
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| Challenge: | Conventionally, lexical relations in word vector space have been defined by collections of relatively consistent relationships, or vector offsets, between word-pairs. |
| Approach: | They propose to use Word2Vec space of word-pairs to find lexical relations . they also demonstrate a method for approximating the presence of syntactic and semantic relations based on word vectors extracted from word embeddings. |
| Outcome: | The proposed method outperforms other validated methods in the presence of noisy offsets. |
A BERT-based Dual Embedding Model for Chinese Idiom Prediction (2020.coling-main)
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| Challenge: | Chinese idioms are special fixed phrases whose meanings are often highly idiomatic and non-compositional. |
| Approach: | They propose to use a BERT-based dual embedding model to encode contextual words and learn dual embeds of the idioms. |
| Outcome: | The proposed model performs better than the existing state of the art on a Chinese idiom cloze dataset. |
BERT-based Cohesion Analysis of Japanese Texts (2020.coling-main)
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| Challenge: | Recent advances in neural networks have significantly improved natural language processing tasks . they include self training-based language models such as BERT . |
| Approach: | They tackle a systematic analysis of cohesion in Japanese texts using BERT models . they find that coreference resolution is different in nature from other tasks . |
| Outcome: | The proposed analysis outperforms existing studies on cohesion in Japanese texts. |
Schema Aware Semantic Reasoning for Interpreting Natural Language Queries in Enterprise Settings (2020.coling-main)
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| Challenge: | Using ontology reasoning to understand natural language is a challenge for QA systems . a recent study shows that ontologies can improve natural language understanding . |
| Approach: | They propose to use ontology reasoning to translate natural language interpretation into a sequence of solvable tasks by an ontologist. |
| Outcome: | The proposed framework achieves better natural language understanding with a 30% accuracy improvement over the current state of natural language query interfaces. |
Multilingual Irony Detection with Dependency Syntax and Neural Models (2020.coling-main)
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Alessandra Teresa Cignarella, Valerio Basile, Manuela Sanguinetti, Cristina Bosco, Paolo Rosso, Farah Benamara
| Challenge: | Several semantic and syntactic devices can be used to express irony, causing the incongruity, determine the clash and play the role of irony triggers within a text. |
| Approach: | They propose to exploit linguistic resources where syntax is annotated according to the Universal Dependencies scheme. |
| Outcome: | The proposed method exploits linguistic resources where syntax is annotated according to the Universal Dependencies scheme. |
What Can We Learn from Noun Substitutions in Revision Histories? (2020.coling-main)
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| Challenge: | Recent work shows that resulting improvements can be modelled computationally, assuming that each revision contributes to the improvement. |
| Approach: | They propose to model improvements in sentences using wikiHow revision histories by assuming that each revision contributes to the improvement. |
| Outcome: | The proposed model fails in cases where humans can resort to factual knowledge or intuitions about the required level of specificity. |
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity (2020.coling-main)
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| Challenge: | Unsupervised pretraining models encode only distributional knowledge encoded in text corpora, incorporated through language modeling objectives. |
| Approach: | They generalize a standard BERT model to a multi-task learning setting and integrate discrete knowledge on word-level semantic similarity into pretraining. |
| Outcome: | The proposed model outperforms the lexically blind “vanilla” model on several language understanding tasks. |
Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages (2020.coling-main)
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Diptesh Kanojia, Raj Dabre, Shubham Dewangan, Pushpak Bhattacharyya, Gholamreza Haffari, Malhar Kulkarni
| Challenge: | a study of 14 Indian languages shows that cognates can be detected by word embeddings . cognates are variants of the same lexical form across languages . |
| Approach: | They propose to use cross-lingual word embeddings to detect cognates among 14 Indian languages . they then evaluate the impact of their method on neural machine translation . |
| Outcome: | The proposed method improves on a dataset of 12 Indian languages . it also improves quality of the extracted cognates by up to 2.76 BLEU . |
Bridging the Gap in Multilingual Semantic Role Labeling: a Language-Agnostic Approach (2020.coling-main)
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| Challenge: | Recent research indicates that taking advantage of complex syntactic features leads to favorable results in Semantic Role Labeling. |
| Approach: | They propose a language-agnostic model that does away with morphological and syntactic features to achieve robustness across languages. |
| Outcome: | The proposed model outperforms the state-of-the-art in all languages of the CoNLL-2009 benchmark dataset. |
On the Helpfulness of Document Context to Sentence Simplification (2020.coling-main)
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| Challenge: | Text simplification is a hot issue in the field of natural language generation (NLG). |
| Approach: | They propose to use Wikipedia context to improve sentence simplification by using neural networks to learn the effects of preceding and following sentences on current sentences. |
| Outcome: | The proposed model outperforms the best performing model on the baseline dataset by 2.46 (7.22%). |
AutoMeTS: The Autocomplete for Medical Text Simplification (2020.coling-main)
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| Challenge: | Semi-automated text simplification approaches can be used to simplify text faster and at a higher quality. |
| Approach: | They propose to use autocomplete to simplify medical texts using aligned English Wikipedia sentences and pretrained neural language models to analyze the additional context. |
| Outcome: | The proposed model outperforms the best individual model by 2.1% and achieves a word prediction accuracy of 64.52%. |
Multi-Word Lexical Simplification (2020.coling-main)
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| Challenge: | In text simplification, individual words are replaced with their simpler equivalents, but single word substitutions do not cover the full complexity of techniques humans use to approach text simulating. |
| Approach: | They propose a task of multi-word lexical simplification in which a sentence is made easier to understand by replacing its fragment with a simpler alternative. |
| Outcome: | The proposed method is based on a purpose-trained neural language model and evaluates against human and resource-based baselines. |
Exploring the zero-shot limit of FewRel (2020.coling-main)
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| Challenge: | Existing methods to extract information from a language model are limited in their ability to generalize and do not perform as well as few-shot learning models. |
| Approach: | They propose a general purpose relation extractor that uses Wikidata descriptions to represent the relation’s surface form. |
| Outcome: | The proposed system is based on a FewRel 1.0 dataset, which provides an excellent framework for training and evaluating the proposed system in English. |
A Deep Generative Distance-Based Classifier for Out-of-Domain Detection with Mahalanobis Space (2020.coling-main)
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| Challenge: | Existing methods for detecting out-of-domain (OOD) intents rely on manually labeled samples . a strong generative distance-based classifier can detect OOD samples in task-oriented dialog systems . |
| Approach: | They propose a generative distance-based classifier to detect out-of-domain (OOD) intents . they use Gaussian discriminant analysis to avoid over-confidence problems . |
| Outcome: | The proposed method outperforms baseline methods on four benchmark datasets. |
Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack (2020.coling-main)
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| Challenge: | Existing approaches to zero-shot slot filling ignore constraints in the latent space and lack robustness. |
| Approach: | They propose a Contrastive Zero-Shot Learning with Adversarial Attack method for slot filling . they propose to map slot value contextual representations to slot description representations . |
| Outcome: | The proposed method outperforms state-of-the-art models under zero-shot and few-shot settings. |
DoLFIn: Distributions over Latent Features for Interpretability (2020.coling-main)
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| Challenge: | Existing approaches to interpret neural networks face a trade-off between a model's usefulness and its complexity. |
| Approach: | They propose a novel approach to achieve interpretability that avoids this trade-off by using probability as the central quantity instead of a fixed quantity. |
| Outcome: | The proposed approach outperforms the classical CNN and BiLSTM classifiers on the SST2 and AG-news datasets. |
Contextual Argument Component Classification for Class Discussions (2020.coling-main)
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| Challenge: | Argument mining systems often consider contextual information when training to perform tasks such as argument component identification, classification, and relation extraction. |
| Approach: | They propose to incorporate speaker context and local discourse context into a model for classifying argument components in multi-party classroom discussions. |
| Outcome: | The proposed model improves when varying context size and position . the results support the claim that context size is important . |
On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages (2020.coling-main)
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| Challenge: | recurrent models have been effective in NLP tasks but performance on context-free languages (CFLs) is weak. |
| Approach: | They evaluate the performance of recurrent models on Dyck-n languages . they find that they are expressive enough to recognize Dyck words of arbitrary lengths if their depths are bounded. |
| Outcome: | The proposed models generalize well on Dyck-n languages, while performing poorly on longer test strings. |
Pre-trained Language Model Based Active Learning for Sentence Matching (2020.coling-main)
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| Challenge: | Existing active learning approaches for natural language processing ignore the characteristics of natural language. |
| Approach: | They propose a pre-trained language model based active learning approach for sentence matching that provides linguistic criteria to measure instances and help select more effective instances for annotation. |
| Outcome: | The proposed approach can achieve greater accuracy with fewer labeled training instances. |
Event-Guided Denoising for Multilingual Relation Learning (2020.coling-main)
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| Challenge: | Existing methods for general purpose relation extraction use a fixed set of predetermined relations, but research has shifted to the identification of unseen relations in any language. |
| Approach: | They propose a method for collecting high quality relation training data for relation extraction from unlabeled text that achieves a near-recreation of their zero-shot and few-shot results at a fraction of the training cost. |
| Outcome: | The proposed method achieves comparable results to the current state-of-the-art when trained on a smaller multilingual encoder . |
Using a Penalty-based Loss Re-estimation Method to Improve Implicit Discourse Relation Classification (2020.coling-main)
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| Challenge: | inessential words are unintentionally misjudged as attention-worthy words and assigned heavier attention weights than should be. |
| Approach: | They propose a penalty-based method to regulate the attention learning process by integrating penalty coefficients into the computation of loss by means of overstability of attention weight distributions. |
| Outcome: | The proposed method improves on the Penn Discourse TreeBank corpus and is competitive compared to the state-of-the-art methods. |
A Review of Dataset and Labeling Methods for Causality Extraction (2020.coling-main)
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| Challenge: | Existing methods for causal relationship extraction are limited and lack of unified methods hinder progress in the field. |
| Approach: | They propose to summarize existing methods and propose a new causal sequence label method . they propose to use multiple candidate causal label sequences according to label controversy . |
| Outcome: | The proposed method summarises existing methods and explores their practicability and extensibility from multiple perspectives. |
Knowledge Graph Embedding with Atrous Convolution and Residual Learning (2020.coling-main)
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| Challenge: | Existing knowledge graph embedding methods are complex and require time for training and inference. |
| Approach: | They propose an atrous convolution based knowledge graph embedding method that increases feature interactions by using atrous . they evaluate method on six benchmark datasets with different evaluation metrics . |
| Outcome: | The proposed method achieves better results on six benchmark datasets than state-of-the-art methods on most evaluation metrics. |
KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision (2020.coling-main)
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| Challenge: | Existing methods of event causality detection use hand-labeled training data. |
| Approach: | They propose a framework for event causality detection that augments training data via distant supervision. |
| Outcome: | The proposed framework outperforms existing methods on two benchmark datasets . it outperformed previous methods by a large margin assisted with automatically labeled training data. |
Graph Enhanced Dual Attention Network for Document-Level Relation Extraction (2020.coling-main)
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| Challenge: | Document-level relation extraction requires inter-sentence reasoning capabilities to capture local and global contextual information for multiple relation facts. |
| Approach: | They propose to characterize the interaction between sentences and potential relation instances via a Graph Enhanced Dual Attention network (GEDA) . they also propose a simple yet effective regularizer based on the natural duality of the S2R and R2S attentions, whose weights are also supervised by the supporting evidence of relation instances during training. |
| Outcome: | The proposed model achieves competitive performance on an existing large-scale dataset while the predictions can be interpretable and easily observed. |
Joint Entity and Relation Extraction for Legal Documents with Legal Feature Enhancement (2020.coling-main)
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| Challenge: | Existing methods for information extraction are based on pipelining to extract entities from unstructured judgment documents . a large number of judgment documents are released on China Judgments Online . |
| Approach: | They propose a legal triplet extraction system for drug-related criminal judgment documents . they annotate a dataset for Named Entity Recognition and Relation Extraction in Chinese legal domain . |
| Outcome: | The proposed system extracts entities and semantic relations jointly and benefits from the proposed legal lexicon feature and multi-task learning framework. |
TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking (2020.coling-main)
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| Challenge: | Existing methods to extract entities and relations from unstructured text are susceptible to cascading errors due to the separation of entity detection and relation classification. |
| Approach: | They propose a one-stage joint extraction model that detects overlapping relations while being immune from exposure bias. |
| Outcome: | The proposed model can identify overlapping relations while being immune from exposure bias. |
TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation (2020.coling-main)
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| Challenge: | Existing knowledge graphs that contain time information for entities and relations have been used for learning and inference. |
| Approach: | They propose a temporal evolution of entity embedding that defines the temporal rotation from the initial time to the current time in the complex vector space. |
| Outcome: | The proposed model outperforms existing state-of-the-art models for link prediction on three different TKGs. |
Meta-Information Guided Meta-Learning for Few-Shot Relation Classification (2020.coling-main)
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| Challenge: | Existing meta-learning models rely on implicit instance statistics and are unreliability and weak interpretability. |
| Approach: | They propose a meta-information guided meta-learning framework that uses semantics to guide meta- learning . experimental results demonstrate the effectiveness of the proposed framework . |
| Outcome: | The proposed framework can establish connections between instance-based information and semantic-based data, enabling faster initialization and adaptation. |
Unsupervised Deep Language and Dialect Identification for Short Texts (2020.coling-main)
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| Challenge: | Existing methods for identifying closely related short texts are unsupervised . however, performance is poor for unsupervised methods for short texts . |
| Approach: | They propose a method which can learn sentence embeddings and cluster assignments from short texts. |
| Outcome: | The proposed method outperforms state-of-the-art methods in supervised settings . it can learn sentence embeddings and cluster assignments from short texts . |
A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification (2020.coling-main)
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| Challenge: | Existing supervised and distantly supervised RC models ignore the emergence of novel relations in open environment. |
| Approach: | They propose a two-phase prototypical network with prototype attention alignment and triplet loss to dynamically recognize the novel relations with a few support instances without catastrophic forgetting. |
| Outcome: | Experiments show that the proposed model performs better on deep learning and few-shot learning . it can recognize the novel relations with a few support instances without catastrophic forgetting . |
Document-level Relation Extraction with Dual-tier Heterogeneous Graph (2020.coling-main)
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| Challenge: | Existing methods focus on extracting relations from single sentence . document-level relation extraction requires a comprehension of the whole document . |
| Approach: | They propose a graph-based model with Dual-tier Heterogeneous Graph (DHG) for document-level relation extraction. |
| Outcome: | The proposed model achieves state-of-the-art performance on two widely used datasets. |
Biased TextRank: Unsupervised Graph-Based Content Extraction (2020.coling-main)
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| Challenge: | TextRank ranks text spans according to their importance for language processing tasks and their relevance to an input “focus.” |
| Approach: | They propose a graph-based content extraction method inspired by TextRank that ranks text spans according to their importance for language processing tasks and according to relevance to an input “focus.” |
| Outcome: | The proposed method improves on two different datasets by significant ROUGE-N score margins. |
Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention (2020.coling-main)
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| Challenge: | Existing approaches to handle wrong labeling and long-tail relations are labor-intensive and scarce training data. |
| Approach: | They propose a neural network to handle wrong labeling and long-tail relations by collaborating relation-augmented attention. |
| Outcome: | The proposed neural network improves the state-of-the-art on the NYT dataset . |
ToHRE: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction (2020.coling-main)
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| Challenge: | Existing methods to find relational facts from texts lack hierarchical information of relations. |
| Approach: | They propose a hierarchical classification framework which extracts relation in a top-down manner. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on NYT dataset . the proposed method generates large amounts of training data by aligning KBs with unlabeled corpora . |
Unsupervised Fact Checking by Counter-Weighted Positive and Negative Evidential Paths in A Knowledge Graph (2020.coling-main)
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| Challenge: | Misinformation spreads across media, community, and knowledge graphs in the Web by human agents and information extraction algorithms. |
| Approach: | They propose a rule-based approach that finds positive and negative evidential paths in a knowledge graph for a given factual statement and calculates a truth score for the given statement by unsupervised ensemble. |
| Outcome: | The proposed approach outperforms the state-of-the-art unsupervised approaches by up to 0.12 AUC-ROC and even outperfies the supervised approach by up 0.05 AUC. |
Improving Relation Extraction with Relational Paraphrase Sentences (2020.coling-main)
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| Challenge: | Existing annotated data is expensive and non-scalable, limiting performance of relation extraction models. |
| Approach: | They propose to enrich relation expressions by relational paraphrase sentences by annotating human-annotated data. |
| Outcome: | The proposed model improves performance even on a strong baseline. |
Autoencoding Improves Pre-trained Word Embeddings (2020.coling-main)
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| Challenge: | Existing work has shown that word embeddings are distributed in a narrow cone and that centering and projection can improve the accuracy of pre-trained word embeds without requiring additional training data. |
| Approach: | They propose to remove the top principal components from pre-trained word embeddings and center and project them onto principal component vectors to reinstate isotropy in the embeddable space. |
| Outcome: | The proposed method is equivalent to applying a linear autoencoder to minimize the squared L2 reconstruction error. |
PoD: Positional Dependency-Based Word Embedding for Aspect Term Extraction (2020.coling-main)
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| Challenge: | Existing word embeddings that capture the contextual information only produce moderate results in aspect term extraction. |
| Approach: | They propose a positional dependency-based word embedding which takes both dependency context and positional context into account for aspect term extraction. |
| Outcome: | The proposed method outperforms other embedding methods in aspect term extraction. |
Unequal Representations: Analyzing Intersectional Biases in Word Embeddings Using Representational Similarity Analysis (2020.coling-main)
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| Challenge: | Specifically, we probe contextualized and non-contextualized word embeddings for evidence of intersectional biases against Black women. |
| Approach: | They propose a representational similarity analysis approach to detect human-like biases in word embeddings using representational similarities analysis. |
| Outcome: | The proposed approach aligns with intersectionality theory, which states that multiple identity categories layer on top of each other to create unique modes of discrimination that are not shared by any individual category. |
Vec2Sent: Probing Sentence Embeddings with Natural Language Generation (2020.coling-main)
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| Challenge: | a new unsupervised probing task is able to retrieve black-box sentence embeddings . a variety of problems surround probing tasks, including manual construing . |
| Approach: | They propose a method to generate black-box sentence embeddings by conditionally generating from them . they also illustrate how the language generated from different encoders differs . |
| Outcome: | The proposed probing task improves the performance of black-box sentence embeddings . the proposed task is based on a conditional natural language generation approach . |
Multi-Task Learning for Knowledge Graph Completion with Pre-trained Language Models (2020.coling-main)
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| Challenge: | Existing knowledge graph completion methods are lacking in ranking metrics such as Hits@k . despite the high performance, the proposed method is still behind state-of-the-art models. |
| Approach: | They propose a multi-task learning method that integrates relational and relevance ranking tasks with target link prediction to improve ranking performance. |
| Outcome: | The proposed method improves ranking performance but still behind state-of-the-art models in Hits@k and Mean Rank metrics. |
comp-syn: Perceptually Grounded Word Embeddings with Color (2020.coling-main)
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Bhargav Srinivasa Desikan, Tasker Hull, Ethan Nadler, Douglas Guilbeault, Aabir Abubakar Kar, Mark Chu, Donald Ruggiero Lo Sardo
| Challenge: | Existing approaches to natural language processing ignore embodied sensory aspects of language. |
| Approach: | They propose a Python package that provides word embeddings based on Google Image search results. |
| Outcome: | The proposed package provides word embeddings based on the color distributions of Google Image search results. |
Try to Substitute: An Unsupervised Chinese Word Sense Disambiguation Method Based on HowNet (2020.coling-main)
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| Challenge: | Existing unsupervised methods for word sense disambiguation cannot work for HowNet-based WSD because of its uniqueness. |
| Approach: | They propose a method which exploits the masked language model task of pre-trained language models to conduct word sense disambiguation using a lexical knowledge base as the sense inventory. |
| Outcome: | The proposed method achieves significantly better performance than baseline methods. |
Combining Event Semantics and Degree Semantics for Natural Language Inference (2020.coling-main)
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| Challenge: | formal semantics has been used to account for the semantics of verb phrases and adverbial modifiers . but it is not clear whether these independent theories can be combined and extended to cases in which the phenomena in question interact. |
| Approach: | They propose a logic-based NLI system that combines event semantics and degree semantics. |
| Outcome: | The proposed system achieves high accuracies on linguistically challenging datasets . the proposed system can handle various combinations of linguistic phenomena without compromise . |
Complaint Identification in Social Media with Transformer Networks (2020.coling-main)
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| Challenge: | Existing work on identifying complaints in social media has focused on feature-based and task-specific neural network models. |
| Approach: | They evaluate a battery of neural models underpinned by transformer networks and combine them with linguistic information to predict complaints. |
| Outcome: | The proposed models outperform state-of-the-art methods on a publicly available dataset achieving a macro F1 up to 87. |
Syntactically Aware Cross-Domain Aspect and Opinion Terms Extraction (2020.coling-main)
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| Challenge: | Supervised-learning approaches fail to scale across domains where labeled data is lacking. |
| Approach: | They propose a method for incorporating external linguistic knowledge into a self-attention mechanism coupled with a transformer-based model. |
| Outcome: | The proposed method enables leveraging syntactic knowledge from transformer-based models to bridge the gap between domains. |
A Deep Generative Approach to Native Language Identification (2020.coling-main)
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| Challenge: | Native language identification (NLI) is a multi-class classification task involving multiple features that capture the systematic fingerprints of the first language in the second language writing. |
| Approach: | They propose a deep generative language modelling approach to NLI that fine-tunes a GPT-2 model separately on texts written by the authors with the same L1 and assigns n-grams to an unseen text. |
| Outcome: | The proposed method outperforms traditional machine learning approaches and currently achieves the best results on the benchmark NLI datasets. |
Modeling Event Salience in Narratives via Barthes’ Cardinal Functions (2020.coling-main)
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| Challenge: | Existing methods for estimating event salience without annotations are prohibitively costly because they require annotators to understand the concept of event salientity. |
| Approach: | They propose to use Barthes’ definition of event salience to compute event salientity without annotations by using a pre-trained language model. |
| Outcome: | The proposed methods outperform baseline methods on folktales with event salience annotation and fine-tuned language model is key factor in improving the methods. |
Medical Knowledge-enriched Textual Entailment Framework (2020.coling-main)
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| Challenge: | Existing approaches to achieving robust medical question answering systems lack a textual entailment framework that can capture the con-text beyond the sentence. |
| Approach: | They propose a medical knowledge-enriched textual entailment framework that can acquire a semantic and global representation of the input medical text with the help of a relevant domain-specific knowledge graph. |
| Outcome: | The proposed framework achieves 8.27% improvement over existing language models on MEDIQA-RQE dataset. |
Predicting Personal Opinion on Future Events with Fingerprints (2020.coling-main)
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| Challenge: | Existing methods to predict users’ opinions on going events may not be able to acquire such content and thus cannot infer an unbiased opinion on emerging events. |
| Approach: | They propose to model opinion on unseen articles based on one’s fingerprinting: the prior reading and commenting history. |
| Outcome: | The proposed model can predict user’s opinion on unseen articles based on one’s fingerprinting: the prior reading and commenting history. |
Detecting de minimis Code-Switching in Historical German Books (2020.coling-main)
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| Challenge: | Code-switching has drawn scholarly attention in computational linguistics and natural language processing from many different perspectives. |
| Approach: | They propose to compare informal code-switching to its appearance in more formal registers by annotating and inspecting the German textarchives. |
| Outcome: | The proposed classifiers can help reduce errors when speech recognition is applied to a large corpus with rare embedded languages. |
Lin: Unsupervised Extraction of Tasks from Textual Communication (2020.coling-main)
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| Challenge: | Identifying tasks from emails and chats is a hallmark of collaborative communication . state-of-the-art approaches for task identification rely on large annotated datasets . |
| Approach: | They propose an unsupervised approach to identifying tasks that leverages dependency parsing and VerbNet. |
| Outcome: | The proposed approach yields comparable or more accurate results than supervised models on unseen domains. |
Connecting the Dots Between Fact Verification and Fake News Detection (2020.coling-main)
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| Challenge: | Existing methods for detecting fake news rely heavily on supervised learning on a large scale dataset with news articles labeled as fake or real by human experts. |
| Approach: | They propose a simple yet effective approach to connect the dots between fact verification and fake news detection by using a text summarization model pre-trained on news corpora to summarize the long news article into a short claim. |
| Outcome: | The proposed approach enables zero-shot fake news detection, alleviating the need for large scale training data to train fake news detector models. |
Personalized Multimodal Feedback Generation in Education (2020.coling-main)
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| Challenge: | In this paper, we propose a novel Personalized Multimodal Feedback Generation Network (PMFGN) that generates personalized feedback for teachers to evaluate assignments involving multimodal inputs. |
| Approach: | They propose a Personalized Multimodal Feedback Generation Network (PMFGN) that generates personalized feedback for teachers to evaluate assignments involving multimodal inputs such as images, audios, and texts. |
| Outcome: | The proposed model outperforms baseline models on real-world K-12 education data and detailed ablation experiments to deepen understanding of the proposed framework. |
Reasoning Step-by-Step: Temporal Sentence Localization in Videos via Deep Rectification-Modulation Network (2020.coling-main)
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| Challenge: | Existing methods for temporal sentence localization in videos focus on visual content, but they are insufficient to model complex video contents. |
| Approach: | They propose a deep rectification-modulation network to correct attention misalignment . they use sentence information to capture frame-to-frame relation . |
| Outcome: | The proposed method achieves state-of-the-art performance on three public datasets. |
RIVA: A Pre-trained Tweet Multimodal Model Based on Text-image Relation for Multimodal NER (2020.coling-main)
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| Challenge: | Named entity recognition (MNER) for tweets is a key task of many applications. |
| Approach: | They propose a pre-trained multimodal named entity recognition model based on Relationship Inference and Visual Attention (RIVA) for tweets. |
| Outcome: | The proposed model improves on the multimodal named entity recognition (MNER) task on tweets with the aid of visual clues. |
Towards Knowledge-Augmented Visual Question Answering (2020.coling-main)
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| Challenge: | Visual Question Answering (VQA) is a challenging task for humans, but it is effortless for visual-based approaches. |
| Approach: | They propose a visual-based approach that captures interactions between visual scenes and external knowledge sources and exploits ConceptNet as the source of general knowledge. |
| Outcome: | The proposed model learns a question-adaptive graph representation of related knowledge instances. |
Visual-Textual Alignment for Graph Inference in Visual Dialog (2020.coling-main)
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| Challenge: | Existing approaches to visual dialog do not understand semantic dependencies between visual and textual contents. |
| Approach: | They propose a Visual-Textual Alignment for Graph Inference network that makes up the lack of structural inference in visual dialog. |
| Outcome: | The proposed model outperforms existing models on a VisDial dataset. |
Ad Lingua: Text Classification Improves Symbolism Prediction in Image Advertisements (2020.coling-main)
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Andrey Savchenko, Anton Alekseev, Sejeong Kwon, Elena Tutubalina, Evgeny Myasnikov, Sergey Nikolenko
| Challenge: | a recent study shows that image-based symbols are insufficient for symbolism prediction in visual advertising . a new method is proposed to help understand image advertisements . |
| Approach: | They propose a multimodal image-based classifier and object detection classifier for symbols . they propose 'symbolic' annotation tasks to help users understand ads' |
| Outcome: | The proposed system establishes state-of-the-art in symbolism prediction. |
Humans Meet Models on Object Naming: A New Dataset and Analysis (2020.coling-main)
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| Challenge: | Existing object naming datasets that use only images with a bounding box are noisy . a human-like model behavior is not stable across domains, a study finds . |
| Approach: | They use MN v2 to verify object naming datasets with dozens of valid names per object . they find that human-like model behavior is not stable across domains . |
| Outcome: | The proposed model confuses people and clothing objects more frequently than humans do. |
Encoding Lexico-Semantic Knowledge using Ensembles of Feature Maps from Deep Convolutional Neural Networks (2020.coling-main)
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| Challenge: | Semantic models derived from visual information have overcome some of the limitations of text-based distributional semantic models. |
| Approach: | They build image-based meta-embeddings from computer vision models which incorporate information from all layers of the network and encode a richer set of semantic attributes. |
| Outcome: | The proposed representations encode a richer set of semantic attributes and yield a more complete representation of human conceptual knowledge. |
Language-Driven Region Pointer Advancement for Controllable Image Captioning (2020.coling-main)
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| Challenge: | Controllable Image Captioning is a recent sub-task of Image Captions wherein constraints are placed on which regions in an image should be described in the generated natural language caption. |
| Approach: | They propose a method for predicting the timing of region pointer advancement by treating the advancement step as a natural part of the language structure via a NEXT-token. |
| Outcome: | The proposed method agrees with ground-truth timing in the Flickr30k Entities test data with a precision of 86.55% and a recall of 97.92%. |
Offensive Language Detection on Video Live Streaming Chat (2020.coling-main)
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| Challenge: | a prototype of a live chat room that detects offensive expressions in live streaming chats is presented . offensive expression detection on social media platforms can provide more protection for users . |
| Approach: | They propose a live chat room that detects offensive expressions in live streaming chats in real time . they used a dataset from Twitch to analyze offensive expression patterns . |
| Outcome: | The proposed chat room detects offensive expressions in live streaming chats in real time. |
Image Caption Generation for News Articles (2020.coling-main)
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| Challenge: | Existing work on news-image captioning requires a joint understanding of image and text. |
| Approach: | They propose a Transformer model that integrates text and image modalities and attends to textual features from visual features in generating a caption. |
| Outcome: | The proposed model outperforms the state-of-the-art model and improves the quality of news-image captions. |
CoNAN: A Complementary Neighboring-based Attention Network for Referring Expression Generation (2020.coling-main)
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| Challenge: | Existing approaches to identify and describe complex daily scenes are limited by ambiguity. |
| Approach: | They propose a Complementary Neighboring-based Attention Network that utilizes visual differences between the target object and its highly-related neighbors as complementary features. |
| Outcome: | The proposed expression outperforms state-of-the-art models on a dataset of 3D objects. |
Mark-Evaluate: Assessing Language Generation using Population Estimation Methods (2020.coling-main)
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| Challenge: | Existing population estimation methods focus on open populations or closed populations, but our methods show a higher correlation to human evaluation than existing metrics on several challenging tasks. |
| Approach: | They propose a family of metrics to assess language generation derived from population estimation methods widely used in ecology. |
| Outcome: | The proposed methods show a higher correlation to human evaluation than existing metrics on several challenging tasks, namely unconditional language generation, machine translation, and text summarization. |
TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching (2020.coling-main)
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| Challenge: | Recent studies show that pre-trained language models can produce informative and fluent text with the help of large-scale datasets, but they suffer insufficient learning problem with limited training data. |
| Approach: | They propose to use table transformation module with template to rewrite structured table in natural language as input for GPT-2 and exploit multi-task learning with two auxiliary tasks to preserve table’s structural information. |
| Outcome: | The proposed model outperforms existing systems on most few-shot settings. |
The ApposCorpus: a new multilingual, multi-domain dataset for factual appositive generation (2020.coling-main)
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| Challenge: | appositives are phrases that appear next to a noun phrase and serve an explicative function. |
| Approach: | They propose a more realistic end-to-end definition of appositive generation with a dataset that spans four languages and two entity types. |
| Outcome: | The proposed model is non-trivial and leaves plenty of room for improvement. |
Generalized Shortest-Paths Encoders for AMR-to-Text Generation (2020.coling-main)
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| Challenge: | a recent effort to encode graphs with gated convolutions provides local context, but distance messages travel is limited by number of encoder propagation steps. |
| Approach: | They propose to use generalized shortest-paths to train a graph encoder to learn shortest paths . they show that path diversity affects performance across levels of AMR connectivity . |
| Outcome: | The proposed model achieves a 1.4 BLEU and 1.8 chrF++ margin over a baseline model that encodes only pairwise-unique shortest paths. |
An Enhanced Knowledge Injection Model for Commonsense Generation (2020.coling-main)
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Zhihao Fan, Yeyun Gong, Zhongyu Wei, Siyuan Wang, Yameng Huang, Jian Jiao, Xuanjing Huang, Nan Duan, Ruofei Zhang
| Challenge: | a recent study shows that digging the relationship of concepts from scratch is non-trivial for commonsense generation tasks. |
| Approach: | They use a retrieve-and-edit framework to retrieve a prototype with these concepts . they use qt and qq to generate commonsense questions at scale . |
| Outcome: | The proposed method significantly improves the performance on commonsense generation tasks. |
Multi-grained Chinese Word Segmentation with Weakly Labeled Data (2020.coling-main)
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| Challenge: | Existing work on single-grained word segmentation (SWS) focuses on segmenting a sentence into multiple word sequences to preserve all words of different granularities. |
| Approach: | They propose to use a graph-based parser to accommodate weakly labeled data for MWS by employing a simple yet competitive graph-basic parsers with local loss. |
| Outcome: | The proposed model outperforms the state-of-the-art model on weakly labeled data on a high-quality dataset from canonical newswire (NEWS) and non-canonical web (BAIKE) data. |
KeyGames: A Game Theoretic Approach to Automatic Keyphrase Extraction (2020.coling-main)
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| Challenge: | Automated keyphrase extraction (AKE) is a task of identifying important words and phrases that best describe a given text document. |
| Approach: | They introduce two advancements in the automatic keyphrase extraction space - KeyGames and pke+. |
| Outcome: | The proposed framework outperforms state-of-the-art models while generalizing better on documents with different domains and length. |
Parsers Know Best: German PP Attachment Revisited (2020.coling-main)
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| Challenge: | PP attachment is one of the major sources of parser errors and is still one of hardest problems for syntactic parsing. |
| Approach: | They present a realistic evaluation of the potential of different PP attachment systems using fully predicted information as system input. |
| Outcome: | The proposed approach is superior to modelling PP attachment disambiguation as a separate task. |
Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning (2020.coling-main)
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| Challenge: | Chinese word segmentation datasets have ambiguous annotation criteria resulting in multi-grained compounds. |
| Approach: | They propose a domain adaptive segmenter to exploit diverse annotation criteria of datasets . they use bidirectional encoder representations from transformers to introduce open-domain knowledge . |
| Outcome: | The proposed model outperforms the state-of-the-art models on 10 Chinese word datasets with superior efficiency. |
Joint Chinese Word Segmentation and Part-of-speech Tagging via Multi-channel Attention of Character N-grams (2020.coling-main)
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| Challenge: | Chinese word segmentation and part-of-speech tagging can be performed in a sequential order . existing studies have shown that jointly performing them can be effective . |
| Approach: | They propose a character-based neural model enhanced by multi-channel attention of n-grams. |
| Outcome: | The proposed model outperforms baseline models on five benchmark datasets. |
Taking the Correction Difficulty into Account in Grammatical Error Correction Evaluation (2020.coling-main)
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| Challenge: | a paper aims to improve performance measures for grammatical error correction . conventional measures treat all errors equally, but some are easier to correct . |
| Approach: | They propose a way to determine the difficulty of error correction and to motivate researchers . paper examines performance measures for grammatical error correction using a scorer and weighting algorithm . |
| Outcome: | The proposed measures agree with our intuition of correction difficulty . the results show that the measures are more complex than conventional measures . |
Automatic Distractor Generation for Multiple Choice Questions in Standard Tests (2020.coling-main)
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| Challenge: | Existing methods to generate distractors for multiple choice questions are expensive and time-consuming. |
| Approach: | They propose a question and answer guided distractor generation framework to automate distractors generated by domain experts. |
| Outcome: | The proposed model outperforms existing models and achieves state-of-the-art on a large-scale dataset. |
Towards A Friendly Online Community: An Unsupervised Style Transfer Framework for Profanity Redaction (2020.coling-main)
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| Challenge: | Existing methods for redacting offensive comments into non-offensive ones are inadequate to detect hateful content on social media platforms. |
| Approach: | They propose a method for transforming offensive comments into non-offensive ones using a Retrieve, Generate and Edit unsupervised style transfer pipeline. |
| Outcome: | The proposed method outperforms existing models on automatic metrics and human evaluations and consistently performs well on all automatic evaluation metrics. |
How Positive Are You: Text Style Transfer using Adaptive Style Embedding (2020.coling-main)
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| Challenge: | Existing approaches for unsupervised text style transfer are disentanglement between content and style. |
| Approach: | They propose to separate a model with a sentence reconstruction module and a style module to improve model architecture. |
| Outcome: | The proposed method improves style transfer performance and content preservation . the proposed method can be used to modify a sentence with a specified style attribute . |
Neural text normalization leveraging similarities of strings and sounds (2020.coling-main)
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| Challenge: | Existing methods that ignore the similarities of word strings and sounds do not account for these features. |
| Approach: | They propose a neural model that considers the similarities of both word strings and sounds, and a model that takes only the similarity of word strings or of sounds as a baseline. |
| Outcome: | The proposed models outperformed a baseline model and achieved state-of-the-art results on WNUT-2015. |
Generating Diverse Corrections with Local Beam Search for Grammatical Error Correction (2020.coling-main)
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| Challenge: | Existing methods of acquiring diverse outputs focus on revising all tokens of a sentence. |
| Approach: | They propose a beam search method to obtain diverse outputs in a local sequence transduction task where most of the tokens in the source and target sentences overlap. |
| Outcome: | The proposed method generates more diverse corrections without losing accuracy in the local sequence transduction task. |
A Neural Local Coherence Analysis Model for Clarity Text Scoring (2020.coling-main)
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| Challenge: | Existing methods for scoring text clarity use local coherence between adjacent sentences . local cohesion is one of the main properties to identify whether a text is well-structured or not. |
| Approach: | They propose a method for scoring text clarity by utilizing local coherence between adjacent sentences. |
| Outcome: | The proposed method improves on the PeerRead benchmark dataset. |
Grammatical error detection in transcriptions of spoken English (2020.coling-main)
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| Challenge: | CrowdED corpus of spoken English monologues on business topics was crowdsourced from native speakers of English and learners of English with German as their first language. |
| Approach: | They propose to use the corpus recordings to correct existing speech transcriptions and edit them to make them more fluent. |
| Outcome: | The proposed transcription corrections and annotations can be used for automatic transcription post-editing and grammatical error correction for spoken English. |
Automatic Assistance for Academic Word Usage (2020.coling-main)
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| Challenge: | Experimental results show that lexical formality analysis helps improve the quality of substitution suggestions over a number of competitive baselines. |
| Approach: | They propose a system that automatically suggests lexical substitutions that aim to incorporate more academic vocabulary. |
| Outcome: | The proposed system improves the quality of substitution suggestions over a baseline that relies on the masked language model. |
Style versus Content: A distinction without a (learnable) difference? (2020.coling-main)
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| Challenge: | Textual style transfer assumes that it is possible to separate style from content . however, style transfer can provide insight into language more generally . |
| Approach: | They propose to use sentiment transfer to examine whether style transfer is possible . they employ adversarial encoder-decoder networks to analyze style-related features . |
| Outcome: | The proposed method combines style transfer with content preservation and fluency to show that style cannot be usefully separated from content within style transfer systems. |
Contextualized Embeddings for Enriching Linguistic Analyses on Politeness (2020.coling-main)
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| Challenge: | Current word embeddings in natural language processing do capture context and thus can be leveraged to enrich linguistic analyses. |
| Approach: | They propose a model which leverages pre-trained BERT to cluster contextualized representations of a word based on context in which it appears and labels of items it occurs in. |
| Outcome: | The proposed model can detect interpretable, finer-grained context patterns associated with (im)polite language. |
Heterogeneous Recycle Generation for Chinese Grammatical Error Correction (2020.coling-main)
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| Challenge: | Recent work in the field of grammatical error correction (GEC) rely on neural machine translation-based models. |
| Approach: | They propose a heterogeneous approach to Chinese grammatical error correction using NMT-based models, sequence editing models, and a spell checker. |
| Outcome: | The proposed model achieves state-of-the-art performance without data augmentation or changes in architecture . it adapts the ERRANT scorer to be able to score Chinese sentences . |
Improving Grammatical Error Correction with Data Augmentation by Editing Latent Representation (2020.coling-main)
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| Challenge: | Existing methods for enhancing grammatical error correction use noise to generate tokens . existing methods only generate sentences with limited error types, which leads to lack of diversity of generated errors. |
| Approach: | They propose a data augmentation method that can apply noise to latent representations of a sentence to generate synthetic samples with various error types. |
| Outcome: | The proposed method improves performance and robustness of existing models on public benchmarks and on FCE benchmarks. |
Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer (2020.coling-main)
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| Challenge: | Existing methods for unsupervised text style transfer lack parallel data and difficulties in content preservation. |
| Approach: | They propose a neural approach to unsupervised text style transfer using non-parallel data. |
| Outcome: | The proposed approach can be trained end-to-end on two widely-used public datasets. |
Ask to Learn: A Study on Curiosity-driven Question Generation (2020.coling-main)
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| Challenge: | Existing work on Question Generation focuses on generating relevant questions given text with an answer . human ability to ask questions goes beyond evaluation of reading comprehension . |
| Approach: | They propose a novel text generation task based on a conversational question-asking dataset . they investigate automated metrics to measure different properties of Curious Questions . |
| Outcome: | The proposed task is based on a conversational Question Answering dataset . the results show that humans tend to ask questions with the goal of obtaining new information . |
Formality Style Transfer with Shared Latent Space (2020.coling-main)
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| Challenge: | Existing approaches for formality style transfer use neural networks for sentence generation, but the dataset for formal style transfer is considerably smaller than translation corpora. |
| Approach: | They propose a new approach for formality style transfer using shared latent space and two auxiliary losses. |
| Outcome: | The proposed approach outperforms baselines in various settings, especially when limited data is available. |
Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication (2020.coling-main)
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| Challenge: | Existing methods for visual storytelling construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content. |
| Approach: | They propose a topic description task to detect the global semantic context of an image stream and a story is then constructed with the guidance of the topic description. |
| Outcome: | The proposed framework can generate stories with higher quality compared to state-of-the-art methods on a VIST dataset. |
Referring to what you know and do not know: Making Referring Expression Generation Models Generalize To Unseen Entities (2020.coling-main)
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| Challenge: | Data-to-text Natural Language Generation (NLG) is a computational process of generating natural language from non-linguistic data. |
| Approach: | They propose two extensions to a state-of-the-art encoder-decoder REG model that generates referring expressions to unseen entities. |
| Outcome: | The proposed model generates more meaningful referring expressions to unseen entities than the original system and related work. |
Topic-driven Ensemble for Online Advertising Generation (2020.coling-main)
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| Challenge: | Existing methods for automating online advertising use open data . subdomains of text vary in use and can lead to reduced quality of adverts generation. |
| Approach: | They propose a neural network-based approach for the automatic generation of online advertising using texts from given webpages as sources. |
| Outcome: | The proposed approach significantly improves the quality of online advertising generated on a Russian dataset. |
Retrieval-Augmented Controllable Review Generation (2020.coling-main)
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| Challenge: | Existing approaches to generate reviews using attribute identifiers are limited and dependent on how well they can capture vector representations of attributes. |
| Approach: | They propose to leverage attributes as inputs for review generation by using reference sets . they propose to use these references to enrich inductive biases of given attributes . |
| Outcome: | The proposed model improves over previous approaches on automatic and human evaluation metrics. |
Automatic Detection of Machine Generated Text: A Critical Survey (2020.coling-main)
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| Challenge: | Current text generative models excel in producing text that matches the style of human language reasonably well. |
| Approach: | They conduct an in-depth error analysis of the state-of-the-art detector and discuss research directions to guide future work in this exciting area. |
| Outcome: | The proposed detectors can distinguish between human and text generated by the model and can be used to generate fake news and fake product reviews. |
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning (2020.coling-main)
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| Challenge: | Paraphrase generation is of great importance for many downstream tasks in natural language processing. |
| Approach: | They propose a method to generate sentences as learning objectives from the learned data distribution and employ reinforcement learning to combine these new learning objectives for model training. |
| Outcome: | The proposed method gains significant diversity and improves generation quality over state-of-the-art datasets. |
Curious Case of Language Generation Evaluation Metrics: A Cautionary Tale (2020.coling-main)
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| Challenge: | a few popular metrics are still used to evaluate language generation systems despite their known limitations. |
| Approach: | They propose to use automatic metrics to evaluate language generation systems . they show that they prefer system outputs to human-authored texts . |
| Outcome: | The proposed metrics are insensitive to correct translations of rare words and can yield high scores when given a single sentence as system output for the entire test set. |
Facts2Story: Controlling Text Generation by Key Facts (2020.coling-main)
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| Challenge: | Existing methods for story generation struggle with staying coherent for long periods of time. |
| Approach: | They propose a controlled generation task which expands a sequence of facts into a longer narrative. |
| Outcome: | The proposed model produces competitive fluency while adhering to the requested facts. |
Story Generation with Rich Details (2020.coling-main)
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| Challenge: | Recent neural story generation systems have been able to produce coherent stories. |
| Approach: | They propose a model that features an outliner, which proceeds the main story line to realize global coherence, and a detailer, which supplies relevant details to the story in a locally coherent manner. |
| Outcome: | The proposed model outperforms baseline models in the informativeness and coherence tests on human participants. |
Learning with Contrastive Examples for Data-to-Text Generation (2020.coling-main)
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Yui Uehara, Tatsuya Ishigaki, Kasumi Aoki, Hiroshi Noji, Keiichi Goshima, Ichiro Kobayashi, Hiroya Takamura, Yusuke Miyao
| Challenge: | Existing models for data-to-text generation generate fluent but sometimes incorrect sentences . Existing studies show that using contrastive examples improves the ability of generating sentences with better lexical choice without degrading the fluency. |
| Approach: | They propose to use models trained on incorrect sentences and learning methods that exploit contrastive examples to reduce such errors. |
| Outcome: | The proposed models generate fluent sentences but often have problematic ones in terms of correctness. |
MedWriter: Knowledge-Aware Medical Text Generation (2020.coling-main)
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Youcheng Pan, Qingcai Chen, Weihua Peng, Xiaolong Wang, Baotian Hu, Xin Liu, Junying Chen, Wenxiu Zhou
| Challenge: | Recent studies focus on the information of unstructured text rather than structured information of the knowledge graph. |
| Approach: | They propose a knowledge-aware text generation model for medical domains that incorporates knowledge graphs into the model to improve the quality of generated text. |
| Outcome: | The proposed model improves the quality of generated text and has robust superiority over other methods. |
Dynamic Topic Tracker for KB-to-Text Generation (2020.coling-main)
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| Challenge: | Existing KB-to-text generation models suffer from an off-topic problem . existing models generate unrelated clauses regardless of input data . |
| Approach: | They propose a dynamic topic tracker that learns a global hidden representation for topics and recognizes the corresponding topic during each generation step. |
| Outcome: | The proposed model improves the performance of sentence generation and mitigates off-topic problem. |
Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation (2020.coling-main)
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| Challenge: | Variational Autoencoders (VAEs) have been widely used in text modelling but posterior collapse is a problem when RNN-based models are employed. |
| Approach: | They propose a timestep-wise regularisation VAE architecture which can effectively avoid posterior collapse when used in text modelling. |
| Outcome: | The proposed model avoids posterior collapse and can be applied to any RNN-based VAE model. |
GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation (2020.coling-main)
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| Challenge: | a large-scale, general-domain dataset is needed for knowledge graph-to-text generation . data collection is expensive and data-intensive, making it difficult to get good annotation . |
| Approach: | They propose to use a large-scale, general-domain dataset to generate unsupervised text from knowledge graphs. |
| Outcome: | The proposed dataset has 1.3M text and graph examples, and is a benchmark for future research . good annotation is expensive and difficult to get, and it's difficult to check quality . |
Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity (2020.coling-main)
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| Challenge: | End-to-end neural data-totext generation has faced challenges generalizing to new domains and generating semantically consistent text. |
| Approach: | They propose a neural data-to-text generation system that makes minimal assumptions about the data representation and target domain. |
| Outcome: | The proposed system achieves state of the art results on four major D2T datasets with better semantic fidelity than the state-of-the-art methods. |
Graph-Based Knowledge Integration for Question Answering over Dialogue (2020.coling-main)
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| Challenge: | Existing approaches for question answering over dialogue did not consider dialogue structure and background knowledge (e.g., relationships between speakers). |
| Approach: | They propose a method which organizes a dialogue as a "relational graph" and uses edges to represent relationships between entities to encode multi-relations knowledge for reasoning. |
| Outcome: | The proposed method is better at tackling complex questions requiring relational reasoning and defending adversarial attacks with distracting sentences. |
A hierarchical approach to vision-based language generation: from simple sentences to complex natural language (2020.coling-main)
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| Challenge: | Automating video to language translation is a challenging problem, but it is unclear what the neural network learns . |
| Approach: | They propose a hierarchical approach to automatically describing videos in natural language . they propose generating video descriptions as sequences of simple sentences followed by a more complex and fluent description in natural languages. |
| Outcome: | The proposed approach generates video descriptions as sequences of simple sentences, followed by a more complex and fluent description in natural language. |
Sentiment Forecasting in Dialog (2020.coling-main)
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| Challenge: | Existing studies on sentiment classification focus on determining polarity of existing utterances. |
| Approach: | They propose a Neural Sentiment Forecasting task which simulates the next utterance based on context and a sequence influence model to learn both pair-wise and seq-wise influence. |
| Outcome: | The proposed model outperforms existing models over several strong baselines. |
I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning (2020.coling-main)
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| Challenge: | a large amount of pre-defined commonsense knowledge is available for commonsensense reasoning . humans acquire commonsence in their lives, but machines cannot learn commonseense without assistance. |
| Approach: | They propose an AMR-ConceptNet-Pruned (ACP) graph that is pruned from a full integrated graph . they show that the ACP graph interprets the reasoning path and predicts the correct answer . |
| Outcome: | The proposed graph outperforms baseline models in the commonsenseQA task . it shows that the reasoning path can be interpreted with the relations and concepts provided by the graph . |
Bracketing Encodings for 2-Planar Dependency Parsing (2020.coling-main)
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| Challenge: | Existing bracketing-based encodings only handle a mild extension of projective trees . encodes that encode arcs in a given plane provide almost total coverage of crossing arc . |
| Approach: | They propose a bracketing-based encoding that can be used to represent any 2-planar dependency tree over a sentence of length n as a sequence of n labels. |
| Outcome: | The proposed method improves over existing bracketing encodings in non-projective treebanks while achieving similar speed. |
Semi-Supervised Dependency Parsing with Arc-Factored Variational Autoencoding (2020.coling-main)
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| Challenge: | Existing methods for dependency parsing use unlabelled data to compensate for the lack of training corpora. |
| Approach: | They propose semi-supervised dependency parsing methods that utilize unlabelled data to compensate for the scarcity of labelled training corpora. |
| Outcome: | The proposed model overcomes the tree constraint and the complexity of the training procedure while avoiding the challenges brought by the tree constraints. |
Multitask Easy-First Dependency Parsing: Exploiting Complementarities of Different Dependency Representations (2020.coling-main)
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| Challenge: | Existing dependency parsing models for Arabic use complementary annotations, CATiB and UD treebanks, and partially created trees for one annotation are also available to the other as features for the score function. |
| Approach: | They propose to use Arabic dependency annotations to parse projective dependency trees using CATiB and UD treebanks. |
| Outcome: | The proposed model gives 9.9% error reduction on CATiB and 6.1% on UD compared to a strong baseline and ablation tests show that the main contribution is given by sharing tree representation between tasks, and not simply sharing biLSTM layers as is often performed in NLP multitask systems. |
Context Dependent Semantic Parsing: A Survey (2020.coling-main)
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| Challenge: | Semantic parsing is the task of translating natural language utterances into machine-readable meaning representations. |
| Approach: | They propose to use contextual information to translate natural language utterances into machine-readable meaning representations. |
| Outcome: | The proposed methods do not utilize contextual information, which could boost the semantic parsing systems. |
A Survey of Unsupervised Dependency Parsing (2020.coling-main)
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| Challenge: | Syntactic dependency parsing is an important task in natural language processing . unsupervised learning of dependency parses requires training sentences to be manually annotated with their correct parse trees. |
| Approach: | They propose to survey existing approaches to unsupervised dependency parsing . they identify two major classes of approaches and discuss recent trends . |
| Outcome: | The proposed methods can be used in semantic parsing, machine translation, relation extraction, and many other tasks. |
Exploring Question-Specific Rewards for Generating Deep Questions (2020.coling-main)
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| Challenge: | Recent question generation approaches use the sequence-to-sequence framework to optimize the log likelihood of ground-truth questions using teacher forcing. |
| Approach: | They propose to optimize for QG-specific objectives via reinforcement learning to improve question quality. |
| Outcome: | The proposed model improves the fluency, relevance, and answerability of generated questions. |
CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answering (2020.coling-main)
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| Challenge: | Existing QA systems deal with factoid questions and assume a simplified setup such as multiple-choice questions, retrieving spans of text from given documents, and filling in the blanks. |
| Approach: | They propose a cross-passage hierarchical memory network for question answering via text generation that extends XLNet by adding an auxiliary memory module to the context memory and answer memory. |
| Outcome: | The proposed architecture outperforms the state-of-the-art generative QA framework with better syntactically well-formed answers and increased precision on the AmazonQA review dataset. |
Improving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning (2020.coling-main)
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| Challenge: | In most applications, users are not able to provide the correct answer to the system, but they are able provide binary (correct, incorrect) feedback. |
| Approach: | They propose feedback-weighted learning based on importance sampling to improve upon an initial supervised system using binary user feedback. |
| Outcome: | The proposed method improves on an initial supervised system, getting close to a fully-supervised system that has access to the same labeled examples in in-domain experiments (QuAC) and matching in out-of-domain experiment (DoQA). |
Modelling Long-distance Node Relations for KBQA with Global Dynamic Graph (2020.coling-main)
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| Challenge: | Existing studies rely on deep graph neural networks (GNNs) to capture rich structural information, but they lack the structural information needed for QA. |
| Approach: | They propose a framework which captures structural information from KBs and models long-distance node relations from two perspectives. |
| Outcome: | The proposed framework models long-distance node relations from two perspectives . it is based on two widely used multi-hop KBQA datasets . |
Improving Commonsense Question Answering by Graph-based Iterative Retrieval over Multiple Knowledge Sources (2020.coling-main)
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| Challenge: | Existing methods to facilitate natural language understanding rarely involve commonsense or background knowledge. |
| Approach: | They propose a question-answering method that integrates multiple knowledge sources to boost performance. |
| Outcome: | The proposed method outperforms other competing methods on the CommonsenseQA dataset and achieves the new state-of-the-art. |
A Vietnamese Dataset for Evaluating Machine Reading Comprehension (2020.coling-main)
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| Challenge: | despite the lack of benchmark datasets for Vietnamese, there are few studies on machine reading comprehension (MRC) . MRC is an essential core for a range of natural language processing applications such as search engines and intelligent agents. |
| Approach: | They propose to use Vietnamese Question Answering Dataset to evaluate machine reading comprehension in Vietnamese . they use over 23,000 human-generated question-answer pairs based on 5,109 Vietnamese articles . |
| Outcome: | The proposed dataset includes over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. |
Improving Spoken Language Understanding by Wisdom of Crowds (2020.coling-main)
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| Challenge: | Existing systems that use statistical approaches to improve spoken language understanding (SLU) lack of training data is an important problem, especially for new system tasks. |
| Approach: | They propose to use crowdsourcing and knowledge community websites to augment the spoken language understanding system by collecting paraphrasing variations for new system tasks and augmented them using similar questions from a knowledge community website. |
| Outcome: | The proposed architecture augmented more than 120,000 samples to improve accuracies even with small seed data. |
Bi-directional CognitiveThinking Network for Machine Reading Comprehension (2020.coling-main)
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| Challenge: | Existing methods for reading comprehension are still in their infancy at the level of cognitive intelligence. |
| Approach: | They propose a bi-directional cognitive knowledge framework to simulate reverse thinking and inertial thinking in the brain to answer questions. |
| Outcome: | The proposed framework shows that bi-directional knowledge helps the QA task. |
Learn with Noisy Data via Unsupervised Loss Correction for Weakly Supervised Reading Comprehension (2020.coling-main)
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| Challenge: | Existing approaches to filter noise for machine reading comprehension (MRC) are difficult to control and introduce noisy data. |
| Approach: | They propose a hierarchical loss correction strategy to avoid fitting noise and enhance clean supervision signals by using an unsupervisedly fitted Gaussian mixture model and a hard bootstrapping loss method. |
| Outcome: | The proposed methods can help improve models significantly on weakly supervised machine reading comprehension datasets. |
Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension (2020.coling-main)
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| Challenge: | Existing methods for machine reading comprehension rely on manually defined features and are difficult to generalize to other tasks. |
| Approach: | They propose a Syntax and Frame Semantics model for Machine Reading Comprehension which takes full advantage of syntax and frame semantics to get richer text representation. |
| Outcome: | The proposed model outperforms ten state-of-the-art models on machine reading comprehension tasks. |
Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure (2020.coling-main)
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| Challenge: | Multiparty dialog applications such as discourse parsing and meeting summarization are now mainstream research. |
| Approach: | They propose to annotate a machine reading comprehension dataset with discourse structure built over multiparty dialog using a modified Segmented Discourse Representation Theory (SDRT) style. |
| Outcome: | The proposed dataset contributes large-scale discourse dependency annotations in a modified Segmented Discourse Representation Theory (SDRT) style for all of its multiparty dialogs, and achieves only 67.7% F1 on Molweni’s questions, a 20+% significant drop as compared against its SQuAD 2.0 performance. |
Joint Event Extraction with Hierarchical Policy Network (2020.coling-main)
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| Challenge: | Existing work on event extraction (EE) is pipelined or uses a joint structure but does not utilize information interactions among event triggers, event arguments, and argument roles. |
| Approach: | They propose to exploit role information of arguments in an event and devise a Hierarchical Policy Network to perform joint EE. |
| Outcome: | The proposed system outperforms existing methods and is more powerful for sentences with multiple events. |
Automated Graph Generation at Sentence Level for Reading Comprehension Based on Conceptual Graphs (2020.coling-main)
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| Challenge: | Using conceptual graphs, we can embed a sentence into a knowledge embedding in a graph to solve slot-filling challenges in question answering and capture neighbouring connections of reference concept nodes. |
| Approach: | They propose a miscellaneous-context-based method to embed a sentence into a directed graph and a task-agnostic semantics module which integrates the syntactic-semantic information. |
| Outcome: | The proposed method can accommodate slot-filling challenges in typical question answering and capture the neighbouring connections of reference concept nodes. |
ForceReader: a BERT-based Interactive Machine Reading Comprehension Model with Attention Separation (2020.coling-main)
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| Challenge: | Various BERT-based reading comprehension models have been proposed, however, these models employ the combined input method without further modification for reading comprehension. |
| Approach: | They propose a BERT-based interactive machine reading comprehension model that uses BERT's combined input method without further modification for reading comprehension. |
| Outcome: | The proposed model improves reading comprehension tasks compared to BERT-based models. |
NUT-RC: Noisy User-generated Text-oriented Reading Comprehension (2020.coling-main)
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| Challenge: | Existing RC models focus on extractive or generative, but ignore integration of them. |
| Approach: | They propose a noisy user-generated text-oriented RC model that integrates extractive and generative RC models by a multi-task learning mechanism and an answer selection module. |
| Outcome: | The proposed model outperforms state-of-the-art models on Twitter. |
To What Degree Can Language Borders Be Blurred In BERT-based Multilingual Spoken Language Understanding? (2020.coling-main)
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| Challenge: | Existing models for multilingual SLU are mostly DNN-based joint models of intent classification and slot filling. |
| Approach: | They propose a BERT-based adversarial model architecture to learn language-shared and language-specific representations for multilingual SLU. |
| Outcome: | The proposed model narrows the gap to the ideal multilingual performance. |
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation (2020.coling-main)
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| Challenge: | Cross-lingual Machine Reading Comprehension (CLMRC) is a challenging problem due to the lack of large-scale annotated datasets in low-source languages, such as Arabic, Hindi, and Vietnamese. |
| Approach: | They propose a novel approach to augment cross-lingual machine reading comprehension by combining knowledge from multiple language branch models into a single model for all target languages. |
| Outcome: | Extensive experiments on two CLMRC benchmarks show the proposed method is effective and robust to data noises. |
Neural Networks approaches focused on French Spoken Language Understanding: application to the MEDIA Evaluation Task (2020.coling-main)
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| Challenge: | Recent studies have focused on English language and tasks, but few have explored the complexity of a SLU task. |
| Approach: | They propose to explore Neural Networks approaches for a French Spoken Language Understanding task. |
| Outcome: | The proposed approach outperforms classical Neural Network Architectures and achieves state-of-the-art results. |
Syntactic Graph Convolutional Network for Spoken Language Understanding (2020.coling-main)
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| Challenge: | Existing work on slot filling and intent detection builds joint models without prior knowledge of linguistic knowledge. |
| Approach: | They propose a joint model that integrates syntactic structure for learning slot filling and intent detection jointly. |
| Outcome: | The proposed model outperforms existing models on two public benchmark datasets and further improves on slot filling and intent detection. |
Conversational Machine Comprehension: a Literature Review (2020.coling-main)
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| Challenge: | Conversational machine comprehension (CMC) is a research track in conversational AI. |
| Approach: | They propose to synthesize a generic framework for CMC models and highlight differences in recent approaches. |
| Outcome: | The proposed model will be used as a compendium for future research. |
Robust Machine Reading Comprehension by Learning Soft labels (2020.coling-main)
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| Challenge: | Neural models have achieved great success on the task of machine reading comprehension, which are typically trained on hard labels. |
| Approach: | They propose a robust training method for machine reading comprehension models to address label sparseness problem by using three strategies to train models on soft labels. |
| Outcome: | The proposed method improves the baseline model performance and achieves state-of-the-art performance on NewsQA and QUOREF. |
Reinforced Multi-task Approach for Multi-hop Question Generation (2020.coling-main)
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| Challenge: | Empirical evaluation shows our model to outperform the single-hop question generation models on both automatic evaluation metrics such as BLEU, METEOR, and ROUGE and human evaluation metrics for quality and coverage of the generated questions. |
| Approach: | They propose a question-aware reward function to maximize the utilization of supporting facts in the context. |
| Outcome: | The proposed model outperforms single-hop neural question generation models on automatic evaluation metrics and human evaluation metrics for quality and coverage of the generated questions. |
Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases (2020.coling-main)
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| Challenge: | Existing methods for question generation over knowledge bases have low diversity and poor fluency due to the limited information contained in the subgraphs and semantic drift due to decoder’s oblivion of the semantics of the answer entity. |
| Approach: | They propose a knowledge-enriched, type-constrained and grammar-guided KBQG model that generates natural-language questions over a set of triples in the KB. |
| Outcome: | The proposed model outperforms existing methods on two widely-used benchmark datasets. |
Adapting a Language Model for Controlled Affective Text Generation (2020.coling-main)
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| Challenge: | Existing models for affective text generation fail to capture emotional aspects of conversations without explicit affective information. |
| Approach: | They propose to incorporate emotion as prior for the probabilistic state-of-the-art text generation model such as GPT-2 and incorporate emotion into the model to ensure grammatical correctness. |
| Outcome: | The proposed model outperforms existing models in all intensities and is robust to human evaluations. |
Generating Instructions at Different Levels of Abstraction (2020.coling-main)
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| Challenge: | Using hierarchical planning, technical instructions can be described at different levels of abstraction. |
| Approach: | They propose a method from AI planning which can capture the structure of complex objects neatly. |
| Outcome: | The proposed method can capture the structure of complex objects neatly. |
“Judge me by my size (noun), do you?” YodaLib: A Demographic-Aware Humor Generation Framework (2020.coling-main)
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| Challenge: | Humor is subjective and can be interpreted in different ways by different people. |
| Approach: | They propose an automatic method for filling the blanks in Mad Libs stories . they build upon the BERT platform to predict location-biased word fillings in incomplete sentences . |
| Outcome: | The proposed framework outperforms a semi-automated approach for filling the blanks in Mad Libs stories while accounting for the demographic backgrounds of the desired audience. |
Does Chinese BERT Encode Word Structure? (2020.coling-main)
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| Challenge: | Existing work has focused on analyzing the features captured by representative models such as BERT . however, little work has investigated word features for character languages such as Chinese . |
| Approach: | They investigate Chinese BERT using attention weight distribution statistics and probing tasks to understand word features. |
| Outcome: | The proposed model improves syntactic, semantic and word sense knowledge on a wide range of NLP tasks. |
Noise Isn’t Always Negative: Countering Exposure Bias in Sequence-to-Sequence Inflection Models (2020.coling-main)
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| Challenge: | Morphological inflection is a sequence-to-sequence task that sees great performance when data is plentiful, but performance falls off sharply in lower-data settings. |
| Approach: | They hypothesize that teacher forcing increases the likelihood that a model too closely models its training data. |
| Outcome: | Experiments show that teacher forcing can overfit models when they enter unknown territory. |
Morphologically Aware Word-Level Translation (2020.coling-main)
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| Challenge: | Current approaches to bilingual lexicon induction (BLI) ignore inflectional morphology . current models degrade when translating less frequent inflected forms . |
| Approach: | They propose a morphologically aware probability model that models lexeme translation and inflectional morphology in a structured way. |
| Outcome: | The proposed model improves performance across 6 language pairs and in the weakly supervised setting. |
Analogy Models for Neural Word Inflection (2020.coling-main)
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| Challenge: | Neural network models are usually very data-hungry and performance of such models can suffer when labeled data is not available. |
| Approach: | They propose to provide models with additional analogy sources to strengthen analogy-formation . they propose to combine the analogy motivated approach with data hallucination or augmentation . |
| Outcome: | The proposed methods improve on state-of-the-art results on 46 languages, especially in low-resource settings. |
Computational Modeling of Affixoid Behavior in Chinese Morphology (2020.coling-main)
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| Challenge: | affixoid behavior in Mandarin Chinese is unclear due to polysemy and diachronic dynamics. |
| Approach: | They propose to use three quantitative features to model affixoid behavior in Mandarin Chinese to determine its status. |
| Outcome: | The proposed model shows that there are no clear criteria that can be used to identify an affix’s status in an isolating language like Mandarin Chinese. |
One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music Comment (2020.coling-main)
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| Challenge: | Existing methods for automatic comment generation generate common and meaningless comments for music. |
| Approach: | They propose a multi-perspective strategy to enhance automatic music comment generation by combining different perspectives on a music comment dataset. |
| Outcome: | The proposed model outperforms state-of-the-art models on two music comment datasets and outperformed existing models by a substantial margin. |
A Tale of Two Linkings: Dynamically Gating between Schema Linking and Structural Linking for Text-to-SQL Parsing (2020.coling-main)
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| Challenge: | Existing methods for text-to-SQL semantic parsing require strict structured prediction due to its application scenario where the output SQL will be sent to an executor program directly. |
| Approach: | They propose to use schema linking and structural linking to link NL to the database schema. |
| Outcome: | The proposed method shows significant gains on the Spider dataset. |
Autoregressive Affective Language Forecasting: A Self-Supervised Task (2020.coling-main)
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| Challenge: | Using natural language, we can model emotional language in time based on past changes of language. |
| Approach: | They propose a task of affective language forecasting to model emotional language in time based on past changes of language. |
| Outcome: | The proposed model outperforms existing models on a Twitter dataset of 1,900 users and scores for 6 emotions and 2 additional linguistic attributes. |
Solving Math Word Problems with Multi-Encoders and Multi-Decoders (2020.coling-main)
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| Challenge: | Existing models that transform text descriptions into equation expressions only consider input/output objects as sequences, ignoring important structural information contained in text descriptions and equation expression. |
| Approach: | They propose a model that uses sequence-based encoders and graph-based decoders to enhance the representation of text descriptions and generate different equation expressions. |
| Outcome: | The proposed model outperforms existing state-of-the-art methods on a dataset with a n-word problem. |
End to End Chinese Lexical Fusion Recognition with Sememe Knowledge (2020.coling-main)
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| Challenge: | a new task for coreference recognition is presented in linguistics . the fusion word is always out-of-vocabulary (OOV) words in downstream paragraph-level tasks . |
| Approach: | They propose a Chinese lexical fusion recognition task which could be regarded as one kind of coreference recognition. |
| Outcome: | The proposed model is effective and competitive for the proposed task. |
Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics (2020.coling-main)
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| Challenge: | a systematic comparative analysis of linguistic meaning representations from different frameworks is needed. |
| Approach: | They compare a rule-based converter and a supervised delexicalized parser to map meaning representations from different frameworks. |
| Outcome: | The proposed method yields surprisingly accurate representations close to fully supervised UCCA parser quality. |
Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification (2020.coling-main)
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| Challenge: | Existing methods to zero-shot relation classification can only identify seen relations . existing methods rely on descriptive information to improve understandability of relation types . |
| Approach: | They propose a logic-guided semantic representation learning model for zero-shot relation classification that builds connections between seen and unseen relations via implicit and explicit semantic representations with knowledge graph embeddings and logic rules. |
| Outcome: | The proposed model can generalize to unseen relation types and achieve promising improvements. |
Semantic Role Labeling with Heterogeneous Syntactic Knowledge (2020.coling-main)
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| Challenge: | Recent work on incorporating syntactic knowledge into neural semantic role labeling has gained much attention . incorporating heterogeneous syntaktic knowledge brings significant improvements over strong baselines . |
| Approach: | They propose to encode heterogeneous syntactic knowledge for SRL from explicit and implicit representations from heterogenous treebanks. |
| Outcome: | The proposed approaches improve on two widely-used benchmark datasets. |
Normalizing Compositional Structures Across Graphbanks (2020.coling-main)
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| Challenge: | Graph-based meaning representations (MRs) exhibit structural differences that reflect different theoretical and design considerations, presenting challenges to uniform linguistic analysis and cross-framework semantic parsing. |
| Approach: | They propose a method to normalize MRs at the compositional level by linguistically-grounded rules. |
| Outcome: | The proposed method increases the match in compositional structure between MRs and improves multi-task learning in a low-resource setting. |
Leveraging WordNet Paths for Neural Hypernym Prediction (2020.coling-main)
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| Challenge: | Existing work on lexical relations based on distributed representations has differed widely. |
| Approach: | They propose a model that generates taxonomy paths for hypernym prediction using WordNet sequences. |
| Outcome: | The hypo2path model outperforms the best model by 4.11 points in hit-at-one (H@1) The proposed model outpersforms previous models by a factor of 0.9. |
When Beards Start Shaving Men: A Subject-object Resolution Test Suite for Morpho-syntactic and Semantic Model Introspection (2020.coling-main)
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| Challenge: | In German, subject-object resolution is the second most error-prone task in syntactic parsing, after prepositional phrase attachment. |
| Approach: | They introduce the SORTS Subject-Object Resolution Test Suite of German minimal sentence pairs for model introspection. |
| Outcome: | The proposed test suite is based on the annotations of 8,502 transitive clauses with 8 word order patterns, 5 morphological and syntactic and 11 semantic property classes. |
Modality Enriched Neural Network for Metaphor Detection (2020.coling-main)
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| Challenge: | Existing methods for detecting metaphors are limited due to ontological differences between metaphorical and non-metaphorical expressions. |
| Approach: | They propose a modality enriched deep learning model that integrates deep learning with effective linguistic insight to detect metaphors in texts. |
| Outcome: | The proposed model improves on previous models and shows that it can be used to detect metaphors in a language context. |
Coordination Boundary Identification without Labeled Data for Compound Terms Disambiguation (2020.coling-main)
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Yuya Sawada, Takashi Wada, Takayoshi Shibahara, Hiroki Teranishi, Shuhei Kondo, Hiroyuki Shindo, Taro Watanabe, Yuji Matsumoto
| Challenge: | a new method for nominal coordination boundary identification is proposed . it uses pre-trained word embeddings to measure similarities of words and detects the span of coordination . |
| Approach: | They propose a method for nominal coordination boundary identification that uses pre-trained word embeddings to measure similarities of words and detects the span of coordination. |
| Outcome: | The proposed method can identify coordination boundaries without training on labeled data . it is comparable to a recent supervised method for the case when the coordinator conjoins simple noun phrases. |
Learning Semantic Correspondences from Noisy Data-text Pairs by Local-to-Global Alignments (2020.coling-main)
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| Challenge: | Existing methods for data-to-text generation use a large-scale training corpus to learn semantic correspondences between structured input data and associated texts. |
| Approach: | They propose a local-to-global alignment framework that uses local and global models to learn semantic correspondences from large-scale datasets. |
| Outcome: | The proposed framework can be generalized to restaurant and computer domains and improve alignment accuracy. |
Definition Frames: Using Definitions for Hybrid Concept Representations (2020.coling-main)
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| Challenge: | a new hybrid representation is proposed that encodes semantic information extracted from definitions. |
| Approach: | They propose a matrix distributed representation extracted from definitions where each dimension is semantically interpretable. |
| Outcome: | The proposed representations have competitive performance with other distributional semantic approaches on word similarity tasks. |
QANom: Question-Answer driven SRL for Nominalizations (2020.coling-main)
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Ayal Klein, Jonathan Mamou, Valentina Pyatkin, Daniela Stepanov, Hangfeng He, Dan Roth, Luke Zettlemoyer, Ido Dagan
| Challenge: | Traditionally, SRL annotations focus on verbal predicates, but other types of predicate are frequent in natural language. |
| Approach: | They propose a semantic scheme for capturing predicate-argument relations for nominalizations, termed QANom, using crowdsourcing and QA-driven annotations. |
| Outcome: | The proposed scheme outperforms existing annotations and is useful for downstream tasks. |
Event Coreference Resolution with their Paraphrases and Argument-aware Embeddings (2020.coling-main)
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| Challenge: | Existing methods for event coreference resolution do not identify paraphrase relations between events. |
| Approach: | They propose a new event-specific paraphrase and argument-aware semantic Embedding model for event coreference resolution based on event-related paraphrases and argument embeddings . EPASE recognizes deep paraphrase relations in an event- specific context of sentences and can cover event paraphrase of more situations . |
| Outcome: | Experiments on within- and cross-document event coreference show it is superior compared to existing methods. |
Studying Taxonomy Enrichment on Diachronic WordNet Versions (2020.coling-main)
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| Challenge: | Ontologies, taxonomies and thesauri are used in many NLP tasks but are often not maintained. |
| Approach: | They propose methods for taxonomy enrichment in a resource-poor setting . they also create novel datasets for training and evaluating taxonomies . |
| Outcome: | The proposed methods are applicable to English and Russian datasets and can be used in other languages. |
VICTR: Visual Information Captured Text Representation for Text-to-Vision Multimodal Tasks (2020.coling-main)
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| Challenge: | Existing text-to-image generation models focus on generating high resolution images and neglect understanding text descriptions. |
| Approach: | They propose a visual contextual text representation which captures rich visual semantic information of objects from text input. |
| Outcome: | The proposed visual contextual text representation improves on the state-of-the-art models. |
Finding the Evidence: Localization-aware Answer Prediction for Text Visual Question Answering (2020.coling-main)
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| Challenge: | Existing text VQA systems generate an answer by selecting from optical character recognition (OCR) texts or a fixed vocabulary. |
| Approach: | They propose a localization-aware answer prediction network that generates the answer and predicts a bounding box as evidence of the generated answer. |
| Outcome: | The proposed network outperforms existing methods on three benchmark datasets for the text VQA task by a noticeable margin. |
Interactive Key-Value Memory-augmented Attention for Image Paragraph Captioning (2020.coling-main)
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| Challenge: | Existing approaches to image paragraph captioning ignore the past alignment information, resulting in repetitive captioning and incomplete captioning. |
| Approach: | They propose an Interactive key-value Memory-augmented Attention model for image paragraph captioning to keep track of attention history along with update-chain of decoder state. |
| Outcome: | Extensive experiments on a benchmark dataset demonstrate the effectiveness of the proposed model. |
Geo-Aware Image Caption Generation (2020.coling-main)
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| Challenge: | Standard image caption generation systems do not take contextual information or world knowledge into account. |
| Approach: | They propose to build an image-specific representation of the geographic context and adapt the caption generation network to produce appropriate geographic names in the image descriptions. |
| Outcome: | The proposed system achieves significant improvements on a dataset that contains contextualized captions and geographic metadata and improves BLEU, ROUGE, METEOR and CIDEr scores. |
Bridge the Gap: High-level Semantic Planning for Image Captioning (2020.coling-main)
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| Challenge: | Recent image captioning models have improved the multi-modal interaction, such as attention mechanisms. |
| Approach: | They propose a high-level semantic planning mechanism that integrates a semantic reconstruction and an explicit order planning mechanism to bridge the gap between visual and language domains. |
| Outcome: | The proposed model outperforms previous methods and achieves the state-of-the-art performance on MS COCO. |
Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition (2020.coling-main)
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| Challenge: | Existing models for discourse relation recognition use self-attention and interactive-attention mechanisms. |
| Approach: | They develop a propagative attention learning model using a cross-coupled two-channel network. |
| Outcome: | The proposed model improves on the baseline models on a Penn Discourse Treebank. |
Dual Attention Model for Citation Recommendation (2020.coling-main)
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| Challenge: | Existing methods for recommending citations suffer from severe information loss . citation recommender methods do not consider the section of the paper for which the user is writing and for which they need to find a citation . |
| Approach: | They propose a novel embedding-based neural network to recommend citations during manuscript preparation. |
| Outcome: | The proposed method can recommend citations during manuscript preparation. |
Dual Attention Network for Cross-lingual Entity Alignment (2020.coling-main)
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| Challenge: | Experimental results on three real-world cross-lingual entity alignment datasets have shown the effectiveness of DAEA. |
| Approach: | They propose a dual attention network for cross-lingual entity alignment . they use relation-aware graph attention and hierarchical attention to solve this problem . |
| Outcome: | The proposed approach can be applied to three real-world cross-lingual entity alignment datasets. |
Task-Aware Representation of Sentences for Generic Text Classification (2020.coling-main)
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| Challenge: | Existing approaches to text classification use a transformer architecture with a linear layer on top. |
| Approach: | They propose a transformer-based approach that outputs a class distribution for a given prediction problem. |
| Outcome: | The proposed model outperforms existing approaches on small training data and can learn to predict new classes even with no training examples. |
RANCC: Rationalizing Neural Networks via Concept Clustering (2020.coling-main)
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| Challenge: | Existing models that construct explanations concurrently with classification predictions are opaque. |
| Approach: | They propose a self-explainable model for Natural Language Processing (NLP) text classification tasks . they extract a rationale from the text and use it to predict a concept of interest . |
| Outcome: | The proposed model can be compressed without complicated compression techniques. |
LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression (2020.coling-main)
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Yihuan Mao, Yujing Wang, Chufan Wu, Chen Zhang, Yang Wang, Quanlu Zhang, Yaming Yang, Yunhai Tong, Jing Bai
| Challenge: | Existing models that use knowledge distillation are memory-intensive and latency-prohibitive . Existing solutions that use this knowledge distilling framework are expensive . |
| Approach: | They propose a solution that uses weight pruning, matrix factorization and knowledge distillation to learn a smaller model. |
| Outcome: | The proposed model reduces the training overheads by an order of magnitude on public datasets while preserving state-of-the-art accuracy. |
Emotion Classification by Jointly Learning to Lexiconize and Classify (2020.coling-main)
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| Challenge: | Existing approaches to identify emotions in short text are limited and lack coverage and inaccuracies when applied to informal short text. |
| Approach: | They propose a novel emotional network to jointly learn sentence emotions and construct emotion lexicons which are dynamically adapted to a given context. |
| Outcome: | The proposed model outperforms several approaches proposed in previous studies and achieves new state-of-the-art on the benchmark Twitter dataset. |
Multi-level Alignment Pretraining for Multi-lingual Semantic Parsing (2020.coling-main)
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| Challenge: | Existing methods for multilingual semantic parsing only handle monolingual parsers, while in real world applications such as Chatbot and search engine, we generally need to handle multi-lingual semanticparsing. |
| Approach: | They propose a multi-level alignment pretraining method in a unified architecture for multi-lingual semantic parsing. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a publicly avail-able multi-lingual semantic parsing dataset and a newly constructed dataset. |
Transformation of Dense and Sparse Text Representations (2020.coling-main)
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| Challenge: | Existing approaches to NLP to leverage sparsity have been limited due to the gap with dense representations. |
| Approach: | They propose a Semantic Transformation method to bridge dense and sparse spaces and propose supervised NLP tasks to use both spaces. |
| Outcome: | Experiments with classification tasks and natural language inference tasks show that the proposed method is effective. |
Conception: Multilingually-Enhanced, Human-Readable Concept Vector Representations (2020.coling-main)
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| Challenge: | Word vector representations suffer from a monolingual bias due to the amount of data available across languages. |
| Approach: | They propose a technique for building language-independent vector representations of concepts which places multilinguality at its core while retaining explicit relationships between concepts. |
| Outcome: | The proposed representations outperform the state-of-the-art in multilingual and cross-lingual Semantic Word Similarity and Word Sense Disambiguation on low-resource languages. |
What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation (2020.coling-main)
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| Challenge: | Acronyms are short forms of phrases that facilitate conveying lengthy sentences in documents. |
| Approach: | They propose to annotate a large dataset for scientific domain and a new deep learning model which expands an ambiguous acronym in a sentence. |
| Outcome: | The proposed model outperforms the state-of-the-art models on the new dataset. |
Sentence Matching with Syntax- and Semantics-Aware BERT (2020.coling-main)
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| Challenge: | Sentence matching aims to determine the special relationship between two sentences. |
| Approach: | They propose to integrate syntactic and semantic information into BERT with sentence matching by using an implicit integration method that is less sensitive to the output structure information. |
| Outcome: | The proposed method achieves state-of-the-art or competitive performance on several sentence matching datasets. |
Temporal Relations Annotation and Extrapolation Based on Semi-intervals and Boundig Relations (2020.coling-main)
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| Challenge: | Existing methods for temporal relations annotation and management are not widely used because they are too complex from the computational perspective. |
| Approach: | They propose a system for the annotation and management of temporal relations that combines the richness and expressiveness of Freksa’s approach with the simplicity of Allen’s notation. |
| Outcome: | The proposed system achieves more agreeable representations of temporal relations without increasing the complexity of the labeling process. |
Homonym normalisation by word sense clustering: a case in Japanese (2020.coling-main)
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| Challenge: | homonyms and homophones are a problem in language processing because of their distinct meanings. |
| Approach: | They propose a method that uses contextualised embeddings to cluster tokens into distinct sense groups and use these groups to normalise synonymous instances to a single representative form. |
| Outcome: | The proposed method is able to normalise synonymous instances to a single representative form in Japanese and improves on normalisation and transliteration. |
Verbal Multiword Expression Identification: Do We Need a Sledgehammer to Crack a Nut? (2020.coling-main)
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| Challenge: | Multiword expressions (MWEs) are word combinations idiosyncratic with respect to syntax or semantics. |
| Approach: | They propose to use a language-independent system to identify previously seen VMWEs by combining filters to obtain the best averaged F-score over 11 languages and the best score for both seen and unseen VMwes. |
| Outcome: | The proposed system obtains the best averaged F-score over 11 languages and even the best score for both seen and unseen VMWEs due to the high proportion of seen VMwes in texts. |
An Unsupervised Method for Learning Representations of Multi-word Expressions for Semantic Classification (2020.coling-main)
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| Challenge: | Existing methods for learning multi-word expressions have language sparsity and are not supervised. |
| Approach: | They propose an unsupervised approach to learning a compositional representation function for multi-word expressions . they use a Tratz dataset to train the composition function on the word-semantic relation . |
| Outcome: | The proposed method outperforms the previous state-of-the-art method on the Tratz dataset with an F1 score of 50.4%. |
SLICE: Supersense-based Lightweight Interpretable Contextual Embeddings (2020.coling-main)
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| Challenge: | Contextualised embeddings are a key component of human languages but their opaqueness makes it difficult to interpret their behaviour. |
| Approach: | They propose a weakly supervised method to learn interpretable embeddings from raw corpora and seed words. |
| Outcome: | The proposed model can represent both a word and its context as embeddings into the same compact space, whose dimensions correspond to interpretable supersenses. |
An Empirical Study of the Downstream Reliability of Pre-Trained Word Embeddings (2020.coling-main)
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| Challenge: | Pre-trained word embeddings have been shown to improve the performance of neural networks across a wide variety of tasks. |
| Approach: | They propose two new metrics to understand the downstream reliability of word embeddings. |
| Outcome: | The proposed model can improve performance with slight changes to the training data, but it can also fail with multiple neural network architectures. |
Sentence Analogies: Linguistic Regularities in Sentence Embeddings (2020.coling-main)
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| Challenge: | Word vectors are often evaluated by assessing to what degree they exhibit regularities with regard to relationships considered in word analogies. |
| Approach: | They propose a number of schemes to induce evaluation data based on lexical analogy data as well as semantic relationships between sentences. |
| Outcome: | The proposed models reflect regularities in lexical analogies and semantic relationships between sentences. |
Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information (2020.coling-main)
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| Challenge: | Recent studies show word embedding models underestimate similarities between similar words and overestimate similarities between distant words. |
| Approach: | They propose two new word embedding methods that align original and re-fined embeddable spaces to a new refined semantic space. |
| Outcome: | The proposed methods outperform state-of-the-art methods for word representation refinement. |
Collective Wisdom: Improving Low-resource Neural Machine Translation using Adaptive Knowledge Distillation (2020.coling-main)
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| Challenge: | Existing approaches to train high-quality NMT models in bilingually low-resource scenarios are limited by the scarcity of parallel sentence-pairs. |
| Approach: | They propose to distill the knowledge of teacher models to a single student model by using knowledge distillation. |
| Outcome: | The proposed approach achieves up to +0.9 BLEU score improvements compared to strong baselines. |
Enabling Interactive Transcription in an Indigenous Community (2020.coling-main)
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| Challenge: | Existing methods for manual transcription are often in isolation from the speech community, and so we miss out on the opportunity to take advantage of the interests and skills of local people. |
| Approach: | They propose a transcription workflow which combines spoken term detection and human-in-the-loop to support speech transcription in almost-zero resource settings. |
| Outcome: | The proposed workflow is based on two endangered languages with zero-resource datasets. |
Optimizing Transformer for Low-Resource Neural Machine Translation (2020.coling-main)
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| Challenge: | Language pairs with limited amounts of parallel data remain a challenge for neural machine translation. |
| Approach: | They propose to optimize a Transformer model for low-resource conditions to improve translation quality by 7.3 BLEU points compared to the default settings. |
| Outcome: | The proposed model improves translation quality up to 7.3 BLEU points compared to the default settings on the IWSLT14 training data compared with the Transformer model. |
Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks (2020.coling-main)
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| Challenge: | Recent work on data augmentation techniques that interpolate inputs and labels shows strong effectiveness in image classification. |
| Approach: | They propose to integrate mixup to transformer-based pre-trained architecture for NLP tasks while keeping the whole end-to-end training system. |
| Outcome: | The proposed framework improves on GLUEbenchmark and transformer-based learning models while keeping the whole end-to-end training system. |
Handling Anomalies of Synthetic Questions in Unsupervised Question Answering (2020.coling-main)
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| Challenge: | Existing approaches to improve unsupervised Question Answering (UQA) are expensive and require additional datasets. |
| Approach: | They propose an unsupervised QA approach that generates QA training data automatically. |
| Outcome: | The proposed method improves unsupervised QA significantly across a number of QA tasks. |
Designing Templates for Eliciting Commonsense Knowledge from Pretrained Sequence-to-Sequence Models (2020.coling-main)
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| Challenge: | Existing approaches to extract implicit knowledge from pretrained models are still unclear. |
| Approach: | They propose to use a template-based approach to extract implicit knowledge for commonsense reasoning on multiple-choice questions. |
| Outcome: | The proposed template can be extended to other MC tasks with contexts such as supporting facts in open-book question answering settings. |
Towards the First Machine Translation System for Sumerian Transliterations (2020.coling-main)
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| Challenge: | Sumerian cuneiform script was invented more than 5,000 years ago and is one of the oldest in history. |
| Approach: | They propose to translate Sumerian texts into English automatically using supervised, phrase-based, and transfer learning techniques. |
| Outcome: | The proposed method accelerates the costly and time-consuming manual translation process and helps researchers better explore the relationships between Sumerian and Mesopotamian culture. |
Using Bilingual Patents for Translation Training (2020.coling-main)
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| Challenge: | While bilingual corpora have been instrumental for machine translation, their utility for training translators has been understudied. |
| Approach: | They propose to use bilingual corpora to train translators in the technical domain. |
| Outcome: | The proposed method improves translation quality of technical terms by concordance with bilingual examples in the in-domain corpus than with general-domain bilingual corpus. |
Federated Learning for Spoken Language Understanding (2020.coling-main)
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| Challenge: | Existing methods to improve robustness of models focus on a single dataset . but, there are few studies on how to combine merits of different datasets . |
| Approach: | They propose a federated learning framework that could unify datasets and tasks . they propose MV-Encoder as backbone of the framework to provide multi-granularity text representations . |
| Outcome: | The proposed framework improves on two SLU benchmark datasets and federated learning settings. |
Attentively Embracing Noise for Robust Latent Representation in BERT (2020.coling-main)
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| Challenge: | Modern digital personal assistants interact with users through voice . high error rates still prevail in the widespread adoption of speech technology . |
| Approach: | They propose to extract more robust latent representations for noisy ASR text classification using transformer tokens and attentive embracement layer and multi-head attention layer. |
| Outcome: | The proposed model significantly outperforms the baseline model on the Chatbot and Snips corpora for intent classification with ASR error. |
A Comprehensive Evaluation of Incremental Speech Recognition and Diarization for Conversational AI (2020.coling-main)
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| Challenge: | Automatic Speech Recognition (ASR) systems are increasingly powerful and more numerous with several options existing as a service. |
| Approach: | They evaluate the most popular automatic speech recognition systems with metrics and experiments designed with these standards in mind. |
| Outcome: | The most popular ASR systems are Microsoft and IBM, and none are suitable for natural spontaneous conversations in real-time. |
Decolonising Speech and Language Technology (2020.coling-main)
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| Challenge: | Indigenous peoples are increasingly unable to go on without speech and language technologies, says a researcher . a postcolonial approach to computational methods for supporting language vitality is needed, says the researcher - lil'watul Lorna Williams . |
| Approach: | They propose to examine colonising discourses in speech and language technology and propose a postcolonial approach to computational methods for supporting language vitality. |
| Outcome: | The paper reviews colonising discourses in speech and language technology and suggests new ways of working with Indigenous communities. |
Dual-decoder Transformer for Joint Automatic Speech Recognition and Multilingual Speech Translation (2020.coling-main)
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| Challenge: | Existing models for automatic speech recognition and multilingual speech translation are on par with cascade counterparts. |
| Approach: | They propose a dual-decoder Transformer architecture that performs automatic speech recognition and multilingual speech translation. |
| Outcome: | The proposed models outperform the previously-reported highest translation performance in multilingual settings and bilingual one-to-one results. |
Multitask Learning-Based Neural Bridging Reference Resolution (2020.coling-main)
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| Challenge: | Existing models for bridging references lack large corpora annotated with briding references . second challenge is different definitions of bridding used in different corpors . |
| Approach: | They propose a multi-task learning-based neural model for bridging reference resolution . they show substantial improvements of up to 8 p.p. on full briding resolution compared to previous models . |
| Outcome: | The proposed model outperforms the best reported results on full bridging resolution by up to 8 p.p. |
Improving Human-Labeled Data through Dynamic Automatic Conflict Resolution (2020.coling-main)
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| Challenge: | a scalable method for estimating the noisiness of labels produced by crowdsourcing annotation tasks is developed. |
| Approach: | They propose a scalable method for estimating the noisiness of labels produced by crowdsourcing semantic annotation tasks and reducing the resulting error by 20-30%. |
| Outcome: | The proposed method reduces the error of the labeling process by 20-30% compared to other common labeling strategies. |
Automatic Discovery of Heterogeneous Machine Learning Pipelines: An Application to Natural Language Processing (2020.coling-main)
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| Challenge: | Existing AutoML systems use heterogeneous techniques to build pipelines that combine techniques and algorithms from different frameworks. |
| Approach: | They propose a system for automatic machine learning that uses heterogeneous techniques. |
| Outcome: | The proposed system is evaluated in diverse machine learning problems and compared with other alternatives. |
Distill and Replay for Continual Language Learning (2020.coling-main)
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| Challenge: | Existing models fail to isolate acquired knowledge and forget previously learned tasks when learning in a stream where data distribution may shift. |
| Approach: | They propose a framework that distills knowledge and replays experience from previous tasks when fitting on a new task. |
| Outcome: | The proposed framework outperforms state-of-the-art models in continuously learning tasks of the same type but from different domains, as well as tasks of different types. |
Incorporating Noisy Length Constraints into Transformer with Length-aware Positional Encodings (2020.coling-main)
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| Challenge: | Neural Machine Translation suffers from an under-translation problem due to limited modeling of output sequence lengths. |
| Approach: | They propose a method to train a Transformer model using length constraints based on positional encoding. |
| Outcome: | The proposed method outperforms a vanilla Transformer in an English-to-Japanese translation by 3.22 points . the noise injection improved robustness for length prediction errors, especially within the window size. |
Rethinking Skip Connection with Layer Normalization (2020.coling-main)
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| Challenge: | Existing methods to solve the optimization problem of deep neural networks are not linear, but can be used as a modulating mechanism between the input and output. |
| Approach: | They propose to use skip connection to adjust the scale of the input and output to improve the performance. |
| Outcome: | The proposed approach improves performance and convergence of deep neural networks and can be applied to machine translation and image classification datasets. |
Specializing Word Vectors by Spectral Decomposition on Heterogeneously Twisted Graphs (2020.coling-main)
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| Challenge: | Word vectors have a tendency to conflate semantic similarity with semantic relatedness . a new method is proposed to retrofit word vectors with lexical constraints . |
| Approach: | They propose a method that heterogeneously retrofits a similarity matrix with lexical constraints. |
| Outcome: | The proposed method has a competitive performance compared with the state-of-the-art methods. |
Deep Inside-outside Recursive Autoencoder with All-span Objective (2020.coling-main)
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| Challenge: | Existing neural approaches for constituency parsing are limited for low-resource languages and domains. |
| Approach: | They extend the training objective of DIORA by making use of all spans instead of only leaf-level spans. |
| Outcome: | The proposed model improves on two languages and provides better parsing accuracy than the original model. |
Cross-Lingual Document Retrieval with Smooth Learning (2020.coling-main)
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| Challenge: | Cross-lingual document search is an information retrieval task in which the queries’ language and the documents’ language are different. |
| Approach: | They propose a robust framework that measures the relevance and a loss function that is a novel objective function. |
| Outcome: | The proposed framework achieves significant gains under commonly used ranking metrics on cross-lingual document retrieval task in a variety of languages. |
Increasing Learning Efficiency of Self-Attention Networks through Direct Position Interactions, Learnable Temperature, and Convoluted Attention (2020.coling-main)
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| Challenge: | SANs are an integral part of successful neural networks such as Transformer . training SAN on a task or pretraining them on language modeling requires large amounts of data and compute resources. |
| Approach: | They propose to modify SANs to enable faster learning, i.e., higher accuracies after fewer update steps. |
| Outcome: | The proposed modifications enable faster learning, i.e., higher accuracies after fewer update steps. |
Picking BERT’s Brain: Probing for Linguistic Dependencies in Contextualized Embeddings Using Representational Similarity Analysis (2020.coling-main)
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| Challenge: | Contextualized word embeddings can incorporate contextual information, whereas other embeddables cannot. |
| Approach: | They propose an approach to address this question using Representational Similarity Analysis (RSA) they investigate whether verb embeddings encode verb’s subject, pronoun embedds antecedent and full-sentence representations encode sentence’s head word . |
| Outcome: | The proposed approach can adjudicate between hypotheses about which aspects of context are encoded in representations of language. |
The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks (2020.coling-main)
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| Challenge: | Contextual embeddings have shown state-of-the-art performance for various tasks such as question answering, sentiment analysis, and textual similarity. |
| Approach: | They propose to integrate transformer-based neural language models into their models to achieve relative improvements of up to 36% on granular tasks. |
| Outcome: | The proposed model outperforms baselines for more granular tasks while outperforming TF-IDF for more complex tasks. |
CoLAKE: Contextualized Language and Knowledge Embedding (2020.coling-main)
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| Challenge: | Existing models for integrating factual knowledge into pre-trained language models are shallow, static, and separately pre-train entities. |
| Approach: | They propose a method which integrates knowledge contexts from large-scale knowledge bases into a unified data structure. |
| Outcome: | The proposed model outperforms existing models on knowledge-driven tasks and knowledge probing tasks. |
Invertible Tree Embeddings using a Cryptographic Role Embedding Scheme (2020.coling-main)
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| Challenge: | Unlike previous attempts, this method does not come at the cost of intractable representation size; it works well when there is sufficient randomness in the representation scheme for simple data and providing an upper bound on its error. |
| Approach: | They propose a method for embedding trees in a vector space based on Tensor-Product Representations (TPRs) that allows for inversion: the retrieval of the original tree structure and nodes from the vectorial embeddment. |
| Outcome: | The proposed method can provide invertibility with error 1% that previous methods would require 8.6 1057 dimensions to represent. |
Synonym Knowledge Enhanced Reader for Chinese Idiom Reading Comprehension (2020.coling-main)
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| Challenge: | Experimental results show that our model achieves state-of-the-art performance for Chinese idiom comprehension. |
| Approach: | They propose a model that can mitigate the inconsistency between literal and literal meanings by incorporating the synonym knowledge enhanced reader into the model. |
| Outcome: | The proposed model achieves state-of-the-art on a Chinese idiom reading comprehension dataset. |
Target Word Masking for Location Metonymy Resolution (2020.coling-main)
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| Challenge: | Existing word sense disambiguation and named entity recognition systems have no explicit metonymy detection. |
| Approach: | They propose an end-to-end word-level classification approach based only on BERT . they show that their approach generalises well to unseen data . |
| Outcome: | The proposed approach surpasses conventional models and benchmarks on 5 datasets and generalises well to unseen data. |
Bridging Resolution: A Survey of the State of the Art (2020.coling-main)
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| Challenge: | bridging resolution is an anaphora resolution task that is less studied than entity coreference resolution. |
| Approach: | This paper presents a survey of the current state of research on bridging resolution . it identifies and resolves bridling/associative anaphors, which are anamorphic references to non-identical associated antecedents. |
| Outcome: | The proposed task is more difficult than entity coreference resolution because of the lack of annotated corpora and lack of standardized evaluation protocols. |
An analysis of language models for metaphor recognition (2020.coling-main)
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| Challenge: | Metaphor recognition systems that are based on language models perform substantially worse on unconventional metaphors than on conventional ones. |
| Approach: | They conduct a linguistic analysis of recent metaphor recognition systems based on language models and a variant of BERT language models to examine their performance. |
| Outcome: | The proposed systems show that they can recognise unseen words if synonyms or morphological variations have been seen before, leading to enhanced generalisation beyond word sense disambiguation. |
What Meaning-Form Correlation Has to Compose With: A Study of MFC on Artificial and Natural Language (2020.coling-main)
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| Challenge: | Compositionality is a widely discussed property of natural languages, although its exact definition has been elusive. |
| Approach: | They propose that compositionality can be measured by measuring meaning-form correlation . they analyze three sets of languages: artificial toy languages tailored to be compositional . |
| Outcome: | The proposed method can assess compositionality on three sets of languages . linguistic phenomena such as synonymy and ungrounded stop-words weigh on the results . |
Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition (2020.coling-main)
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| Challenge: | Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP). |
| Approach: | They compare three transformer-based names to two non-transformer-based ones . they find transformer-derived models incrementally outperform non-tranformer models . |
| Outcome: | The proposed models outperform the studied models in most domains with respect to the F1 score. |
Seeing Both the Forest and the Trees: Multi-head Attention for Joint Classification on Different Compositional Levels (2020.coling-main)
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| Challenge: | Neural networks can capture expressive language features, but insights into the link between words and sentences are difficult to acquire automatically. |
| Approach: | They propose a deep neural network architecture that explicitly wires lower and higher linguistic components and evaluate its ability to perform the same task at different hierarchical levels. |
| Outcome: | The proposed model outperforms equivalent models that are not incentivized towards compositional representations. |
A Unifying Theory of Transition-based and Sequence Labeling Parsing (2020.coling-main)
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| Challenge: | Existing parsers that read sentences from left to right are not learning to parse them. |
| Approach: | They propose a mapping from transition-based parsing algorithms that read sentences from left to right to sequence labeling encodings of syntactic trees. |
| Outcome: | The proposed algorithms are learnable and comparable to existing encodings. |
Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining (2020.coling-main)
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| Challenge: | Discourse parsing is an important upstream task within the area of Natural Language Processing (NLP) . |
| Approach: | They propose a discourse parser that incorporates recent contextual language models to improve the performance of RST-based discourse parses. |
| Outcome: | The proposed parser outperforms existing models on two key RST datasets and on large-scale "silver-standard" discourse treebank MEGA-DT. |
Semi-supervised Domain Adaptation for Dependency Parsing via Improved Contextualized Word Representations (2020.coling-main)
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| Challenge: | Recent advances in deep neural network models have improved parsing performance on in-domain texts . however, the problem is to improve performance on out-of-domain text data when there is only a small-scale out-domain labeled data. |
| Approach: | They propose to use adversarial learning and fine-tuning BERT to improve contextualized word representations on out-of-domain texts. |
| Outcome: | The proposed models achieve consistent improvement and fine-tune BERT processes boost parsing accuracy by a large margin. |
Data Augmentation via Subtree Swapping for Dependency Parsing of Low-Resource Languages (2020.coling-main)
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| Challenge: | Lack of annotated training data is a big issue for building reliable NLP systems for most of the world’s languages. |
| Approach: | They propose a method to swap subtrees between annotated sentences while enforcing strong constraints on those trees to ensure maximum grammaticality of the new sentences. |
| Outcome: | The proposed method outperforms previous methods using the same inputs and using low-resource languages. |
Porous Lattice Transformer Encoder for Chinese NER (2020.coling-main)
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| Challenge: | Existing methods to integrate word boundary information into character-level Chinese NER are inefficient and lack semantic interaction. |
| Approach: | They propose an extension of transformer encoder that is tailored for ChineseNER to incorporate lexicons into character-level Chinese NER by lattices. |
| Outcome: | The proposed extension performs 11.4 times faster than state-of-the-art methods while retaining the rich long-term dependencies. |
Learning to Prune Dependency Trees with Rethinking for Neural Relation Extraction (2020.coling-main)
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| Challenge: | Existing approaches to remove noise from dependency trees are not optimal due to complexity and variability of natural language. |
| Approach: | They propose a dynamically pruned Graph Convolutional Network (DP-GCN) that prunes the dependency tree with rethinking in an end-to-end scheme. |
| Outcome: | The proposed model achieves impressive results compared to strong competitors. |
How Far Does BERT Look At: Distance-based Clustering and Analysis of BERT’s Attention (2020.coling-main)
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| Challenge: | Recent work on multi-head attention mechanism shows heuristics and clues in analyzing various aspects of the mechanism. |
| Approach: | They propose to cluster attention heatmaps into significantly different patterns through unsupervised clustering on top of a set of proposed features. |
| Outcome: | The proposed features can explain and calibrate different attention heads in Transformer models. |
An Analysis of Simple Data Augmentation for Named Entity Recognition (2020.coling-main)
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| Challenge: | Recent studies have focused on using data augmentation techniques on sentence-level and sentence-pair natural language processing tasks such as text classification. |
| Approach: | They propose to use data augmentation techniques for named entity recognition to increase model performance. |
| Outcome: | The proposed techniques boost performance for both recurrent and transformer-based models, especially for small training sets. |
Semi-supervised Autoencoding Projective Dependency Parsing (2020.coling-main)
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| Challenge: | Existing models for semi-supervised dependency parsing use labeled data, but they require large amounts of labeles. |
| Approach: | They propose two end-to-end autoencoding models for semi-supervised graph-based projective dependency parsing. |
| Outcome: | The proposed models outperform a semi-supervised model on WSJ and UD dependency parsing data sets. |
Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers (2020.coling-main)
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| Challenge: | Existing methods of cross-lingual parser transfer focus on predicting the best parsers for a low-resource target language globally. |
| Approach: | They propose a cross-lingual parser transfer paradigm that uses instance-level parsers to predict the best parsing for a target language at treebank level. |
| Outcome: | The proposed model outperforms existing models on 13/20 and 14/20 test languages. |
Learning from Non-Binary Constituency Trees via Tensor Decomposition (2020.coling-main)
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| Challenge: | a binarisation procedure changes the structure of constituency trees, furthering constituents that are not binary. |
| Approach: | They propose a binarised approach to binarise constituency trees by tensor-based models . they propose 'trunk-LSTM' model which exploits such a rich structure . |
| Outcome: | The proposed model performs well on different NLP tasks. |
Second-Order Unsupervised Neural Dependency Parsing (2020.coling-main)
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| Challenge: | supervised dependency parsers can reach a very high accuracy, but they require treebanks for training. |
| Approach: | They propose a second-order extension of unsupervised neural dependency models that incorporate grandparent-child or sibling information. |
| Outcome: | The proposed model achieves 10% improvement over the previous state-of-the-art model on the full WSJ dataset. |
Integrating Domain Terminology into Neural Machine Translation (2020.coling-main)
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| Challenge: | Existing work on terminology integration into Neural Machine Translation shows it can dynamically specialize translation to a specific domain. |
| Approach: | They extend existing work on terminology integration into Neural Machine Translation . they use placeholders complemented by morphosyntactic annotation to integrate terminology . |
| Outcome: | The proposed method surpasses the surface generalization shown by other techniques. |
Understanding the effects of word-level linguistic annotations in under-resourced neural machine translation (2020.coling-main)
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| Challenge: | Using word-level linguistic annotations in under-resourced neural machine translation is challenging for many languages. |
| Approach: | They propose to use word-level linguistic annotations to label source-language (SL) or target-language words to improve translation performance. |
| Outcome: | The proposed language annotations outperform part of speech and morphological description tags in the target language, while the morpho-syntactic description tags improve the grammaticality of the output. |
Breeding Gender-aware Direct Speech Translation Systems (2020.coling-main)
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| Challenge: | In automatic speech translation, traditional cascade approaches involving separate transcription and translation steps are giving ground to more robust direct solutions. |
| Approach: | They compare different approaches to inform direct ST models about the speaker’s gender and test their ability to handle gender translation from English into Italian and French. |
| Outcome: | The proposed models outperform strong but gender-unaware direct ST models in the translation of English into Italian and French. |
Neural Machine Translation Models with Back-Translation for the Extremely Low-Resource Indigenous Language Bribri (2020.coling-main)
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| Challenge: | a small dataset of 5923 Bribri-Spanish pairs is used to train low-resource NMT models . |
| Approach: | They propose a Chibchan NMT model and dataset with an average performance of BLEU 16.91.7 for Bribri. |
| Outcome: | The proposed model improves on the Bribri dataset by 1.0 BLEU, but only when the new Spanish sentences belong to the same domain as the other Spanish examples. |
Dynamic Curriculum Learning for Low-Resource Neural Machine Translation (2020.coling-main)
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| Challenge: | Recent work on neural machine translation (NMT) has demonstrated impressive performance improvements and became the de-facto standard. |
| Approach: | They propose a dynamic curriculum learning method to reorder training samples in training using a Transformer-based system. |
| Outcome: | The proposed method outperforms baselines on three low-resource machine translation benchmarks and different sized data of WMT’16 En-De. |
Real-Valued Logics for Typological Universals: Framework and Application (2020.coling-main)
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| Challenge: | a framework for typological statements captures the truth value of a formula on a given data source. |
| Approach: | They propose a framework which captures the empirical truth value of a formula on a given data source. |
| Outcome: | The proposed framework can be used to express typological statements on multilingual treebanks with comparable annotation. |
Comparative Probing of Lexical Semantics Theories for Cognitive Plausibility and Technological Usefulness (2020.coling-main)
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| Challenge: | lexical semantics theories advocate that the meaning of words is represented as an inference graph, a feature mapping or a cooccurrence vector. |
| Approach: | They propose to probe different lexical semantics theories for their levels of cognitive plausibility and technological usefulness. |
| Outcome: | The proposed approach is superior to the other approaches, the authors argue . they show that the feature-based approach is more useful and more plausible than the other ones . |
CxGBERT: BERT meets Construction Grammar (2020.coling-main)
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| Challenge: | lexico-semantic elements capture a large amount of linguistic information, but they do not capture all information contained in text. |
| Approach: | They propose to use BERT to train a model that uses a deep bidirectional transformer to capture a significant amount of lexico-semantic information. |
| Outcome: | The proposed model captures lexico-semantic information, but it is redundantly encoded in lexical information. |
How LSTM Encodes Syntax: Exploring Context Vectors and Semi-Quantization on Natural Text (2020.coling-main)
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| Challenge: | LSTMs are widely used to capture informative long-term syntactic dependencies, but how they are reflected in their internal vectors for natural text has not been adequately investigated. |
| Approach: | They analyze how syntactic dependencies are reflected in LSTM's internal gates by learning a language model where syntaktic structures are implicitly given. |
| Outcome: | The proposed model can predict whether a word is inside a phrase structure or not from a small number of components of the context-update vector. |
Corpus-based Identification of Verbs Participating in Verb Alternations Using Classification and Manual Annotation (2020.coling-main)
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| Challenge: | Verb alternations allow verbs to appear in a set of syntactically different constructions whose associated semantic frames are systematically related. |
| Approach: | They use ENCOW and VerbNet data to train classifiers to predict the instrument subject alternation and the causative-inchoative alternation . they use count-based and vector-based features as well as perplexity-based language model features to reflect each alternation’s felicity by simulating it. |
| Outcome: | The proposed approach reduces the required annotation effort by only presenting annotators with the highest-scoring candidates from the previous classification. |
When and Who? Conversation Transition Based on Bot-Agent Symbiosis Learning Network (2020.coling-main)
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| Challenge: | a bot-agent symbiosis is a method for transparent conversation transition in online customer service applications. |
| Approach: | They propose a bot-agent symbiosis approach to solve conversation transition problems . they provide user feedback and develop deep neural networks to predict the NPS . |
| Outcome: | The proposed approach outperforms state-of-the-art methods on real-time data generated from an online service support platform. |
Topic-relevant Response Generation using Optimal Transport for an Open-domain Dialog System (2020.coling-main)
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| Challenge: | Conventional neural generative models generate safe and generic responses which have little connection with previous utterances semantically and would disengage users in a dialog system. |
| Approach: | They propose a method that employs topical constraint and semantic constraint to generate relevant responses by regularizing the decoding objective function with semantic distance. |
| Outcome: | The proposed method generates more topic-relevant and content-rich responses than conventional models. |
An Iterative Emotion Interaction Network for Emotion Recognition in Conversations (2020.coling-main)
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| Challenge: | Emotion recognition in conversations (ERC) is a task that aims to recognize the emotion of each utterance in conversations. |
| Approach: | They propose an iterative emotion interaction network which uses iterativly predicted emotion labels instead of gold emotion labels to explicitly model the emotion interaction. |
| Outcome: | The proposed method retains state-of-the-art performance on two datasets and achieves high accuracy. |
PEDNet: A Persona Enhanced Dual Alternating Learning Network for Conversational Response Generation (2020.coling-main)
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| Challenge: | Existing persona-based dialogue models generate personalized responses using predefined persona information, but they lack personality. |
| Approach: | They propose a persona-based dual Alternating Learning Network that generates personalized responses using predefined persona information. |
| Outcome: | The proposed method produces more personalized responses than baseline methods. |
Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems (2020.coling-main)
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| Challenge: | Existing task-oriented dialog systems struggle to dynamically model long dialog context for interactions and effectively incorporate knowledge base (KB) information into dialog generation. |
| Approach: | They propose a dual dynamic memory network for multi-turn dialog generation . the model dynamically expands the dialog memory turn by turn and keeps track of dialog history . |
| Outcome: | The proposed model outperforms baseline models on three benchmark datasets on human evaluation and automatic evaluation. |
Translation vs. Dialogue: A Comparative Analysis of Sequence-to-Sequence Modeling (2020.coling-main)
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| Challenge: | Existing models for machine translation and dialogue response generation require a large number of handcrafted features. |
| Approach: | They propose to interpret a general neural model comparatively by using the seq2seq model in two mainstream NLP tasks. |
| Outcome: | The proposed model is used in two mainstream NLP tasks and is compared with a standard model. |
Diverse dialogue generation with context dependent dynamic loss function (2020.coling-main)
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| Challenge: | Dialogue systems using deep learning have achieved generation of fluent response sentences to user utterances, but they tend to produce responses that are not diverse and less context-dependent. |
| Approach: | They propose an Inverse N-gram loss function which incorporates contextual fluency and diversity at the same time by a simple formula. |
| Outcome: | The proposed loss function outperforms baseline models in automatic evaluations such as DIST-N and ROUGE and achieves higher scores on human evaluations of coherence and richness. |
Towards Topic-Guided Conversational Recommender System (2020.coling-main)
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| Challenge: | Existing CRS datasets focus on immediate requests from users, while lack proactive guidance to the recommendation scenario. |
| Approach: | They propose a topic-guided conversational recommendation dataset . it incorporates topic threads to enforce natural semantic transitions towards the recommendation scenario . |
| Outcome: | The proposed approach is more reasonable and controllable than previous approaches. |
Intent Mining from past conversations for Conversational Agent (2020.coling-main)
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| Challenge: | Conversational systems are of primary interest in the AI community . many commercial bot building frameworks require a collection of user utterances and corresponding intent to train an intent model. |
| Approach: | They propose an intent discovery framework that can mine a vast amount of conversational logs and generate labeled data sets for training intent models. |
| Outcome: | The proposed framework can mine conversational logs and generate labeled data sets for training intent models. |
Summarize before Aggregate: A Global-to-local Heterogeneous Graph Inference Network for Conversational Emotion Recognition (2020.coling-main)
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| Challenge: | Existing studies focus on modeling emotion influences with utterance-level features, with little attention paid on phrase-level semantic connection between utterrances. |
| Approach: | They propose a two-stage Summarization and Aggregation Graph Inference Network which integrates inference for topic-related emotional phrases and local dependency reasoning over neighbouring utterances in a global-to-local fashion. |
| Outcome: | The proposed model outperforms the state-of-the-art models on three CER benchmark datasets. |
Deconstruct to Reconstruct a Configurable Evaluation Metric for Open-Domain Dialogue Systems (2020.coling-main)
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| Challenge: | Existing evaluation metrics are not designed to cope with this flexibility. |
| Approach: | They propose to group the qualities into three groups to obtain a single metric called USL-H. |
| Outcome: | The proposed metric achieves good correlations with human judgment and maintains its configurability towards different aspects and metrics. |
Suggest me a movie for tonight: Leveraging Knowledge Graphs for Conversational Recommendation (2020.coling-main)
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| Challenge: | Recent studies show that knowledge graphs are incomplete since they do not contain all factual information present on the web. |
| Approach: | They propose to use knowledge graphs to improve the performance of conversational recommender systems by incorporating pre-trained embeddings from subgraphs and positional embeddments into their models. |
| Outcome: | The proposed method improves by 5.62% over the state-of-the-art method on multiple metrics on the recommendation task. |
HiTrans: A Transformer-Based Context- and Speaker-Sensitive Model for Emotion Detection in Conversations (2020.coling-main)
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| Challenge: | Emotion detection in conversations is to detect the emotion for each utterance in conversations that have multiple speakers. |
| Approach: | They propose a transformer-based context- and speaker-sensitive model for EDC . they utilize a low-level transformer to generate local utterance representations . |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark datasets. |
A Co-Attentive Cross-Lingual Neural Model for Dialogue Breakdown Detection (2020.coling-main)
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| Challenge: | Existing models for dialogue breakdown detection do not focus on preventing dialogue breakdowns. |
| Approach: | They propose a model that integrates a pretrained cross-lingual language model and a co-attention network for dialogue breakdown detection. |
| Outcome: | The proposed model outperforms all previous approaches on evaluation metrics in Japanese and English tracks in Dialogue Breakdown Detection Challenge 4 . |
Integrating User History into Heterogeneous Graph for Dialogue Act Recognition (2020.coling-main)
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| Challenge: | Existing models cannot fully recognize the specific expressions given by users due to the informality and diversity of natural language expressions. |
| Approach: | They propose a Heterogeneous User History graph convolution network which utilizes the user’s historical answers grouped by DA labels as additional clues to recognize the DA label of utterances. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on two benchmark datasets and shows that it integrates user’s historical answers. |
A Two-Level Interpretation of Modality in Human-Robot Dialogue (2020.coling-main)
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| Challenge: | modal expressions are used to communicate and align world knowledge, but there is no obvious manner to ground them in the shared environment. |
| Approach: | They propose a two-level annotation scheme for modality that captures both content and intent and a task-oriented, pragmatic representation that maps to our robot's capabilities. |
| Outcome: | The proposed model can be grounded and dynamically interpreted. |
Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training (2020.coling-main)
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| Challenge: | Unsupervised neural machine translation (UNMT) has attracted great interest in the machine translation community. |
| Approach: | They propose to explicitly take noisy data into consideration to improve the robustness of UNMT based systems. |
| Outcome: | The proposed methods significantly improved the robustness of the conventional UNMT systems in noisy scenarios. |
Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English (2020.coling-main)
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| Challenge: | Recent work shows that deeper character-based neural machine translation models outperform subword-based models. |
| Approach: | They propose to investigate the ability of character-based models to learn word senses and morphological inflections and the attention mechanism in Finnish into English translation. |
| Outcome: | The character-based models outperform subword-based model in Finnish to English translation. |
Improving Low-Resource NMT through Relevance Based Linguistic Features Incorporation (2020.coling-main)
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| Challenge: | Existing studies on incorporating arbitrary syntactic information into neural machine translation (NMT) are lacking. |
| Approach: | They propose to integrate linguistic knowledge at different levels into neural machine translation framework to improve translation quality for language pairs with extremely limited data. |
| Outcome: | The proposed methods improve translation quality for all tasks by 3.09 BLEU points . the proposed methods are based on two different approaches . |
Layer-Wise Multi-View Learning for Neural Machine Translation (2020.coling-main)
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| Challenge: | Existing approaches to neural machine translation are limited to the topmost encoder layer’s context representation and cannot perceive the lower encoder layers. |
| Approach: | They propose a layer-wise multi-view learning approach to solve this problem by incorporating an auxiliary view into the model. |
| Outcome: | The proposed model can achieve stable results over multiple strong baselines and is agnostic to network architectures. |
Bilingual Subword Segmentation for Neural Machine Translation (2020.coling-main)
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| Challenge: | Existing subword segmentation methods tokenize sentences without considering translation . proposed method could be more favorable to machine translation if it uses bilingual sentences . |
| Approach: | They propose a subword segmentation method that tokenizes sentences by using subword units induced from bilingual sentences. |
| Outcome: | The proposed method improves translation performance on translation tasks up to +0.81 BLEU. |
Token Drop mechanism for Neural Machine Translation (2020.coling-main)
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| Challenge: | Neural machine translation models are vulnerable to unfamiliar inputs. |
| Approach: | They propose to drop tokens of the input sentences to improve generalization and avoid overfitting for the NMT model. |
| Outcome: | The proposed approach improves on Chinese-English and English-Romanian benchmarks and achieves significant performance improvements over baselines. |
Supervised Visual Attention for Multimodal Neural Machine Translation (2020.coling-main)
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| Challenge: | Existing studies show that a conventional visual attention mechanism trained in an unsupervised manner is not effective for multimodal neural machine translation. |
| Approach: | They propose a supervised visual attention mechanism for multimodal neural machine translation that captures the relationship between a word and an image region more precisely than a conventional visual attention system. |
| Outcome: | The proposed model improves on English-German and German-English translation tasks and English-Japanese and Japanese-English tasks using the Flickr30k Entities JP dataset. |
Investigating Catastrophic Forgetting During Continual Training for Neural Machine Translation (2020.coling-main)
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| Challenge: | Neural machine translation models suffer from catastrophic forgetting during continual training . models tend to overfit to frequent observations in the in-domain data but forget previously learned knowledge. |
| Approach: | They investigated the causes of catastrophic forgetting in NMT models by examining their parameters and modules. |
| Outcome: | The proposed model forgets previously learned knowledge and swings to fit new data . the results show that some parameters are important for both the general-domain and in-domain translation and the great change of them during continual training brings about the performance decline in general- domain. |
The Two Shades of Dubbing in Neural Machine Translation (2020.coling-main)
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| Challenge: | Dubbing has two shades; synchronisation constraints are applied only when the actor’s mouth is visible on screen, while the translation is unconstrained for off-screen dubbing. |
| Approach: | They annotate an existing dubbing corpus for this dichotomy and find that on-screen dubbing is more difficult for MT than off-screen. |
| Outcome: | The results show that on-screen dubbing is more difficult for MT than off-screen translation, and that synchronisation constraints dramatically decrease translation quality for off- screen dubbing. |
Filtering Back-Translated Data in Unsupervised Neural Machine Translation (2020.coling-main)
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| Challenge: | Current state of the art approaches for unsupervised neural machine translation (NMT) use only monolingual data for training. |
| Approach: | They propose an approach to filter back-translated data as part of the training process of unsupervised neural machine translation (NMT) they propose a weight component based on the quality of pseudo parallel sentence pairs generated in back-translation phase. |
| Outcome: | The proposed approach improves the training performance of unsupervised neural machine translation systems by giving weight to good pseudo parallel sentence pairs in the back-translation phase. |
Lost in Back-Translation: Emotion Preservation in Neural Machine Translation (2020.coling-main)
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| Challenge: | MT is used to support human-to-human communication across languages, but it is unclear whether it can translate the non-propositional level of emotions. |
| Approach: | They propose to use a re-ranking approach to change emotions to reverse this tendency . they find that emotions are toned down or amplified through linguistic changes . |
| Outcome: | The proposed model can be used to change emotions, and it can be applied to other languages. |
Intermediate Self-supervised Learning for Machine Translation Quality Estimation (2020.coling-main)
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| Challenge: | Existing methods for machine translation quality estimation (QE) rely on annotated data. |
| Approach: | They propose a self-supervised learning task for machine translation (MT) that orients a pre-trained model towards the target task. |
| Outcome: | The proposed method outperforms existing methods on English-to-German and English- to-Russian translation directions and is comparable to existing models. |
Unifying Input and Output Smoothing in Neural Machine Translation (2020.coling-main)
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| Challenge: | Recent methods that smooth input and output of neural machine translation systems bring significant improvements in performance. |
| Approach: | They propose a method that replaces one-hot representations with soft posterior distributions of an external language model, smoothing the input of machine translation systems. |
| Outcome: | The proposed method improves translation performance on small datasets and larger datasets. |
Neural Transduction for Multilingual Lexical Translation (2020.coling-main)
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| Challenge: | a method for completing multilingual translation dictionaries is proposed . a 27% relative improvement in whole-word accuracy is achieved when multilingual data is unavailable . |
| Approach: | They propose a method for completing multilingual translation dictionaries using multilingual inputs and multilingual decoding objective. |
| Outcome: | The proposed method can synthesize new word forms in multilingual translation dictionaries . it can perform in settings where correct translations have not been observed in text . |
A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information (2020.coling-main)
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| Challenge: | Recent studies have shown that inter-sentence information is helpful for improving the performance of document-level Neural Machine Translation models, but what information should be regarded as context remains ambiguous. |
| Approach: | They propose a cache-based document-level NMT model which conducts dynamic caching guided by theme-rheme information. |
| Outcome: | The proposed model achieves substantial improvements over the state-of-the-art models on NIST evaluation sets. |
Context-Aware Cross-Attention for Non-Autoregressive Translation (2020.coling-main)
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| Challenge: | Existing studies have shown that non-autoregressive translation models can predict all tokens independently and simultaneously. |
| Approach: | They propose to enhance signals of neighbour source tokens into conventional cross-attention to address a locality perception problem in NAT cross- attention. |
| Outcome: | The proposed approach improves translation quality over strong NAT baselines on representative datasets. |
Does Gender Matter? Towards Fairness in Dialogue Systems (2020.coling-main)
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| Challenge: | Recent studies have shown that AI is unfair in many real-world applications such as computer vision and recommendations. |
| Approach: | They propose to use a benchmark dataset to study the fairness of dialogue systems to understand their bias. |
| Outcome: | The proposed methods reduce the bias in dialogue systems significantly. |
Combining Cognitive Modeling and Reinforcement Learning for Clarification in Dialogue (2020.coling-main)
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| Challenge: | In many domains, dialogue systems need to work collaboratively with users to reconstruct meaning . this requires a system that can give targeted, effective feedback about the system’s understanding . |
| Approach: | They propose a system that collaborates on reference tasks that distinguish arbitrarily varying color patches from similar distractors and use crowd workers to test their approach. |
| Outcome: | The proposed system can distinguish varying color patches from distractors and elicit correct answers that the system understands. |
Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer (2020.coling-main)
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| Challenge: | Existing models for ERTC use a few non-neutral categories to identify the emotion of each utterance. |
| Approach: | They propose a novel Knowledge Aware Incremental Transformer with Multi-task Learning to address these challenges by leveraging commonsense knowledge to leverage context. |
| Outcome: | The proposed model outperforms state-of-the-art models across five benchmark datasets. |
MEISD: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations (2020.coling-main)
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| Challenge: | Emotion and sentiment classification in dialogues has gained popularity in recent times . a number of datasets are imbalanced in representing different emotions and consist of an only single emotion. |
| Approach: | They propose to use a dataset to analyze emotions and sentiments in dialogues . they use text, audio and video to identify the correct emotions with the appropriate intensity and sentiment in an utterance of a dialogue . |
| Outcome: | The proposed datasets are balanced in representing different emotions and consist of only one emotion. |
EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation (2020.coling-main)
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| Challenge: | Existing work on empathetic dialogue generation fails to capture the nuances of human emotion and consider the potential of user feedback. |
| Approach: | They propose a multi-resolution adversarial model - EmpDG - to generate more empathetic responses by exploiting both coarse-grained dialogue-level and fine-grounded token-level emotions. |
| Outcome: | The proposed model outperforms the state-of-the-art models in both content quality and emotion perceptivity. |
Leveraging Discourse Rewards for Document-Level Neural Machine Translation (2020.coling-main)
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| Challenge: | Document-level machine translation models are often not trained to explicitly ensure discourse quality. |
| Approach: | They propose a method that explicitly optimizes lexical cohesion and coherence metrics by using a reinforcement learning objective. |
| Outcome: | The proposed approach improves document translations over four different languages and three translation domains while maintaining faithfulness to the reference translation. |
Effective Use of Target-side Context for Neural Machine Translation (2020.coling-main)
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| Challenge: | Existing methods to train NMT systems with noisy data are not sufficient . et al., 2018) found that NMT models can learn with multiple types of corpora . |
| Approach: | They propose a Japanese-English 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 efficient than existing methods. |
Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity (2020.coling-main)
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| Challenge: | Existing methods to integrate knowledge graph (KG) with neural machine translation (NMT) have two problems: knowledge under-utilization and granularity mismatch. |
| Approach: | They propose a multi-task learning method on sub-entity granularity to combine machine translation and knowledge reasoning tasks. |
| Outcome: | The proposed method significantly outperforms baseline models on translation tasks and handling the entities. |
Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation (2020.coling-main)
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| Challenge: | Recent studies have revealed a number of pathologies of neural machine translation systems. |
| Approach: | They propose to use maximum a posteriori decoding to identify the highest-scoring translation, i.e. the mode problem, to validate the model and its training algorithm. |
| Outcome: | The proposed model reproduces the statistical data well, but the beam search strays from the statistics. |
Domain Transfer based Data Augmentation for Neural Query Translation (2020.coling-main)
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| Challenge: | Query translation (QT) is a critical factor in successful cross-lingual information retrieval (CLIR). |
| Approach: | They propose to extend query translation (QT) with a domain transfer procedure to revise synthetic candidates to search-aware examples. |
| Outcome: | The proposed method outperforms baselines and domain transfer methods on translation quality and retrieval accuracy. |
Living Machines: A study of atypical animacy (2020.coling-main)
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Mariona Coll Ardanuy, Federico Nanni, Kaspar Beelen, Kasra Hosseini, Ruth Ahnert, Jon Lawrence, Katherine McDonough, Giorgia Tolfo, Daniel CS Wilson, Barbara McGillivray
| Challenge: | atypical animacy is the property of being alive, but discrepancies are not uncommon . a typical animate is represented as either animate or inanimate in a text . |
| Approach: | They propose a method for determining whether an entity is represented as animate in a text . they use a nineteenth-century English text to analyze animacy . |
| Outcome: | The proposed method improves on an established animacy dataset and a newly introduced resource. |
Aspectuality Across Genre: A Distributional Semantics Approach (2020.coling-main)
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| Challenge: | Existing studies have focused on the aspectual class of verbs in English for predicting coherence relations in text and imagery, predicting links in entailment graphs and interpreting sign languages. |
| Approach: | They propose to model two elementary aspects of aspectual class, states vs. events, and telic v. atelic events, with distributional semantics. |
| Outcome: | The proposed model outperforms previous work on three datasets and shows that telicity correlates with genre and discourse goals. |
Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing (2020.coling-main)
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| Challenge: | Existing work on argument quality (AQ) focuses on overall quality, but there is no large-scale theory-based corpus and corresponding computational models. |
| Approach: | They propose to use a large-scale English multi-domain argumentative writing corpus annotated with theory-based AQ scores to assess argument quality. |
| Outcome: | The proposed methods improve argument quality in three domains and can be used as strong baselines for future work. |
A Linguistic Perspective on Reference: Choosing a Feature Set for Generating Referring Expressions in Context (2020.coling-main)
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| Challenge: | Various studies have raised the question of which factors play a role in the choice of referring expressions. |
| Approach: | They propose to use Feature Importance Ranking and Sequential Forward Search to evaluate the “importance” of features in machine learning algorithms for selecting the form of a referring expression in discourse context. |
| Outcome: | The proposed feature set includes 6 features from 4 classes, namely grammatical role, inherent features of the referent, antecedent form and recency. |
Coreference information guides human expectations during natural reading (2020.coling-main)
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| Challenge: | Existing theories of coreference processing focus on memory, but some theories focus on expectations. |
| Approach: | They hypothesize that coreference tracking also informs human expectations about upcoming words. |
| Outcome: | The proposed coreference-aware parser improves human response times in a naturalistic reading experiment. |
Interactive Word Completion for Morphologically Complex Languages (2020.coling-main)
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| Challenge: | morphologically complex languages have multiple morph slots with large or unbounded sets of fillers. |
| Approach: | They propose a method for morphologically-aware text input in Kunwinjku . they modify an existing finite state recognizer to map input morph prefixes to morph completions . |
| Outcome: | The proposed method is portable to Turkish and shows that it can be used in other languages. |
Joint Persian Word Segmentation Correction and Zero-Width Non-Joiner Recognition Using BERT (2020.coling-main)
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| Challenge: | a set of rules are set by the regulatory body of the Persian language regarding the use of white space and ZWNJ . only a few people follow these rules in writing formal Persian, let alone the informal language. |
| Approach: | They address problems of word segmentation and zero-width non-joiner recognition in Persian . they use a macro-averaged F1 score of 92.40% on a carefully collected corpus of 500 sentences . |
| Outcome: | The proposed problem is a sequence labeling problem in Persian . it achieves a macro-averaged F1 score of 92.40% on a carefully collected corpus of 500 sentences . |
Syllable-based Neural Thai Word Segmentation (2020.coling-main)
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| Challenge: | Word segmentation is a challenging pre-processing step for Thai Natural Language Processing due to the lack of explicit word boundaries. |
| Approach: | They propose a neural Thai word segmenter that uses syllable embeddings to capture linguistic constraints and dilated CNN filters to capture the environment of each character. |
| Outcome: | The proposed system outperforms the previous state-of-the-art system on both in-domain and out-domain datasets. |
Incorporating Inner-word and Out-word Features for Mongolian Morphological Segmentation (2020.coling-main)
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| Challenge: | Mongolian morphological segmentation is a crucial preprocessing step in many Mongolian related NLP applications. |
| Approach: | They propose a neural network incorporating inner-word and out-word features for Mongolian morphological segmentation. |
| Outcome: | The proposed network is compared with baselines and evaluates its performance. |
Morphological disambiguation from stemming data (2020.coling-main)
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| Challenge: | morphologically rich languages require ambiguous analysis to be effective . morphology tools are limited for morphlogical analysis, disambiguation, and annotation . |
| Approach: | They propose to learn to morphologically disambiguate Kinyarwanda verbal forms from a crowd-sourced stemming dataset using feature engineering and a feed-forward neural network based classifier. |
| Outcome: | The proposed method achieves about 89% non-contextualized disambiguation accuracy from a crowd-sourced dataset. |
Revitalization of Indigenous Languages through Pre-processing and Neural Machine Translation: The case of Inuktitut (2020.coling-main)
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| Challenge: | Indigenous languages have been considered low-resource and/or endangered . authors propose a method to revitalize the language spoken in northern canada . |
| Approach: | They propose to revitalize the Inuktitut language through pre-processing and neural machine translation . they propose to use this technique to perform morphological analysis and neural translation tasks . |
| Outcome: | The proposed approach improves the Inuktitut language compared to the state-of-the-art . the proposed approach is based on preprocessing and neural machine translation . |
Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities (2020.coling-main)
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| Challenge: | Domain names such as openresearch are being added to a growing set of tokens that an NLP system may need to deal with. |
| Approach: | They propose a tagging model that uses characters as input to break domain names into component words . they propose taagging methods that use concatenated entity names in a large knowledge database . |
| Outcome: | The proposed model improves on concatenated entity names in a knowledge database by 33% . the proposed model can be used for a wide range of languages, including Chinese and Japanese . |
Utilizing Subword Entities in Character-Level Sequence-to-Sequence Lemmatization Models (2020.coling-main)
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| Challenge: | a novel character-level sequence-to-sequence lemmatization model uses generic n-gram embeddings to map word/lemma pairs . semitic languages, like Arabic and Hebrew, add other challenges to handle unseen words . |
| Approach: | They propose a character-level sequence-to-sequence lemmatization model . they use generic n-gram embeddings, concatenative (stems) and templatic (roots and patterns) morphological subwords. |
| Outcome: | The proposed model outperforms other linguistically-driven models with generic n-gram embeddings . the best system handles word/lemma pairs that are both unseen in the training data . |
Wiktionary Normalization of Translations and Morphological Information (2020.coling-main)
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| Challenge: | We extend the Yawipa Wiktionary Parser to extract and normalize translations from etymology glosses and morphological form-of relations. |
| Approach: | They extend Yawipa to extract and normalize translations from etymology glosses . they propose a method to identify typos in translation annotations based on extracted morphological data . |
| Outcome: | The proposed method improves on a standard attention baseline by using copy attention. |
Detecting Urgency Status of Crisis Tweets: A Transfer Learning Approach for Low Resource Languages (2020.coling-main)
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| Challenge: | We train monolingual and cross-lingual classifiers on the extracted features of tweets . we use a few state-of-the-art contextual embeddings to extract features of the tweets. |
| Approach: | They propose to use tweets to train a dataset of English and two low-resource languages to train zero-shot transfer models. |
| Outcome: | The proposed model performs well in English and in low-resource languages . the proposed model is based on state-of-the-art embeddings and semi-supervised methods . |
Cross-lingual Transfer Learning for Grammatical Error Correction (2020.coling-main)
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| Challenge: | Existing studies on English GEC have focused on improving it, but the resources required to train the models are not sufficient. |
| Approach: | They investigate cross-lingual transfer learning in grammatical error correction tasks . similarities between these languages is a key factor for successfully transferring grammatikal knowledge . |
| Outcome: | The proposed methods improve accuracy of grammatical error correction tasks in English and Russian, but lack the resources to train models in these languages. |
Emergent Communication Pretraining for Few-Shot Machine Translation (2020.coling-main)
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| Challenge: | state-of-the-art models that rely on multilingual pretrained encoders achieve sample efficiency in downstream applications, but lack abundant amounts of unlabelled text. |
| Approach: | They propose a method to pretrain neural networks via emergent communication from referential games by grounding communication on images as a crude approximation of real-world environments. |
| Outcome: | The proposed method significantly improves machine translation in few-shot settings and provides an evaluation protocol to probe the properties of emergent languages ex vitro. |
ContraCAT: Contrastive Coreference Analytical Templates for Machine Translation (2020.coling-main)
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| Challenge: | Recent high scores on pronoun translation suggest current approaches work well . et al., 2018: are context-aware nmt models learning this task? |
| Approach: | They propose a test set to assess the ability to handle specific steps for pronoun translation . they propose heuristics that break down when translations require real reasoning . |
| Outcome: | The proposed model can model complex inferences required for translation of english into german . it shows that current approaches are not able to model all of this information well . |
SpanAlign: Sentence Alignment Method based on Cross-Language Span Prediction and ILP (2020.coling-main)
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| Challenge: | Existing methods for automatic sentence alignment assume monotonic alignments, but they can handle non-monotonic alignments. |
| Approach: | They propose a method to automatically extract parallel sentences from noisy parallel documents by embeddings and encoding each source and target sentence. |
| Outcome: | The proposed method improves translation accuracy by 4.1 BLEU scores on English-Japanese . it can predict spans in target document from sentences in source document . |
CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)
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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan
| Challenge: | Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages . |
| Approach: | They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models . |
| Outcome: | The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English . |
A Human Evaluation of AMR-to-English Generation Systems (2020.coling-main)
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| Challenge: | a recent human evaluation of AMR generation systems is compared to automated metrics. |
| Approach: | They propose a human evaluation which collects fluency and adequacy scores and categorization of error types for AMR generation systems. |
| Outcome: | The results show that human evaluations are more nuanced than automated metrics. |
Identifying Annotator Bias: A new IRT-based method for bias identification (2020.coling-main)
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| Challenge: | An important factor that can affect the Inter Annotator Agreement (IAA) is the presence of annotator bias. |
| Approach: | They propose a new interpretation and application of the Item Response Theory to detect annotators' bias and characterise annotation disagreement. |
| Outcome: | The proposed method can be used to spot outliers, improve annotation guidelines and provide a better picture of the annotation reliability. |
Would you describe a leopard as yellow? Evaluating crowd-annotations with justified and informative disagreement (2020.coling-main)
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| Challenge: | Existing evaluation methods rely on agreement between annotators, which implies a single correct interpretation. |
| Approach: | They propose an agreement-independent quality metric based on answer-coherence to evaluate on expected disagreement. |
| Outcome: | The proposed model shows that agreement is the most important indicator of quality in semantic annotation tasks. |
Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis (2020.coling-main)
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| Challenge: | Existing methods to learn general language representations from large volumes of unlabeled text have been used to improve multilingual NLP. |
| Approach: | They propose to use a spatial arrangement method to generate large-scale evaluation datasets that balance cross-lingual alignment with language specificity. |
| Outcome: | The proposed method produces semantic verb classes and fine-grained similarity scores for nearly 130 thousand verb pairs. |
Hierarchical Trivia Fact Extraction from Wikipedia Articles (2020.coling-main)
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| Challenge: | Existing methods for extracting trivia facts for Wikipedia categories are not efficient . a trivia fact is an interesting fact that is unusual, unexpected, or unique . |
| Approach: | They propose an unsupervised algorithm that automatically mines trivia facts for a given entity . they propose to target at a single Wikipedia article and leverage its hierarchical structure . |
| Outcome: | The proposed algorithm outperforms existing methods and is 100 times faster than existing methods. |
Predicting Clickbait Strength in Online Social Media (2020.coling-main)
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| Challenge: | Clickbaits are sensational, provocative or controversial posts that entice readers to click on them. |
| Approach: | They propose to model clickbait strength prediction using transformers to predict clickbaiting intensity. |
| Outcome: | The proposed model outperforms existing methods on a benchmark dataset with 39K posts on 3K posts. |
GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion (2020.coling-main)
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| Challenge: | Parliamentary debates are a valuable language resource for analyzing comprehensive options in a functional, free society. |
| Approach: | They propose a neural model for political speech sentiment analysis exploiting semantic representations and relations between debate transcripts, motions, and political party members. |
| Outcome: | The proposed model exploits semantic representations and relations between debate transcripts, motions, and political party members to predict political polarity and polarities. |
Measuring Correlation-to-Causation Exaggeration in Press Releases (2020.coling-main)
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| Challenge: | Recent studies have found that press releases are a major source of exaggeration in science communication, which is later spread to mainstream media. |
| Approach: | They propose an NLP approach to identify exaggerated causal claims in health press releases that report on observational studies. |
| Outcome: | The proposed approach can identify causal claims in press releases that report on observational studies. |
Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models (2020.coling-main)
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| Challenge: | a study examines the impact of political ideology biases in training data . topic detection methods may contain or propagate certain biase resulting in a skewed data collection . |
| Approach: | They propose to learn a text representation that is invariant to political ideology while still judging topic relevance. |
| Outcome: | The proposed model can be invariant to political ideology while still judging topic relevance. |
A Taxonomy of Empathetic Response Intents in Human Social Conversations (2020.coling-main)
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| Challenge: | Open-domain conversational agents or chatbots are becoming increasingly popular in the natural language processing community. |
| Approach: | They aim to combine dialogue act/intent modelling and neural response generation to produce a large-scale taxonomy for empathetic response intents. |
| Outcome: | The proposed method improves the response quality of chatbots and makes them more controllable and interpretable. |
A Multitask Active Learning Framework for Natural Language Understanding (2020.coling-main)
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| Challenge: | Existing studies have shown that relation information between intents and slots can improve the efficiency of active learning algorithms. |
| Approach: | They propose a multitask active learning framework that exploits relation information between sub-tasks provided by a joint model. |
| Outcome: | The proposed framework achieves competitive performance with less training data than baseline methods on all datasets. |
Two-level classification for dialogue act recognition in task-oriented dialogues (2020.coling-main)
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| Challenge: | Existing methods for dialogue act classification are limited and feature sets are low . recognizing dialogue acts is useful for identifying type of information and knowledge to be conveyed . |
| Approach: | They propose a 2-level classification technique, distinguishing between generic and specific dialogue acts (DA) they propose an efficient approach for specific DA, based on high-level linguistic features. |
| Outcome: | The proposed method outperforms classical methods for DA classification by including high-level features. |
Balanced Joint Adversarial Training for Robust Intent Detection and Slot Filling (2020.coling-main)
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| Challenge: | Existing joint models for intent detection and slot filling show insufficient robustness . however, some small changes of inputs can fool the models to produce wrong predictions . |
| Approach: | They propose a joint adversarial training model that generates adversarials to attack the joint model and trains the model to defend against the adversarial examples. |
| Outcome: | The proposed model achieves significantly higher scores and improves robustness on two datasets. |
Reasoning Requirements for Indirect Speech Act Interpretation (2020.coling-main)
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| Challenge: | Existing systems that pretrain word and sentence embeddings to account for nearby linguistic context are unclear how to integrate extra-linguistic context into NLU. |
| Approach: | They perform a corpus analysis to develop a representation of the knowledge and reasoning used to interpret indirect speech acts. |
| Outcome: | The proposed model is based on the domain-general patterns of reasoning involved and implements Answer Set programming. |
Understanding Unnatural Questions Improves Reasoning over Text (2020.coling-main)
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| Challenge: | Complex question answering (CQA) requires large amounts of human-annotated data . learning effective CQA requires large amount of human annotated . |
| Approach: | They propose to map human-generated questions into unnatural machine-generated ones . they generate synthetic pairs and train a parser that associates synthetic questions with their corresponding action sequences. |
| Outcome: | The proposed model outperforms the state-of-the-art model trained on human-labeled data. |
An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution (2020.coling-main)
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| Challenge: | Existing methods to augment labeled data are limited by the scarcity of labeles . a method called contextual data augmentation (CDA) can be used to augment labels . |
| Approach: | They propose a data augmentation method that generates labeled training instances using a pretrained language model. |
| Outcome: | The proposed method can improve the quality of augmented training data compared to the conventional method. |
A Large-Scale Corpus of E-mail Conversations with Standard and Two-Level Dialogue Act Annotations (2020.coling-main)
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| Challenge: | e-mail conversations have domain-agnostic and two-level dialogue act annotations . et al. (2017): a better understanding of asynchronous conversations. |
| Approach: | They present a large-scale corpus of e-mail conversations with domain-agnostic and two-level dialogue act annotations . they use ISO standard 24617-2 as the annotation scheme to annotate over 6,000 messages and 35,000 sentences . |
| Outcome: | The proposed model outperforms other neural networks but falls short of human performance. |
Intra-/Inter-Interaction Network with Latent Interaction Modeling for Multi-turn Response Selection (2020.coling-main)
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| Challenge: | Existing methods for multi-turn response selection are not practical as the turns of conversations vary. |
| Approach: | They propose to use latent interaction modeling to model multi-level interactions between utterance and response. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three multi-turn response selection benchmark datasets. |
Few-shot Pseudo-Labeling for Intent Detection (2020.coling-main)
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| Challenge: | Existing methods for labeling intents are expensive and time-consuming. |
| Approach: | They propose a folding/unfolding hierarchical clustering algorithm which assigns weighted pseudo-labels to unlabeled user utterances. |
| Outcome: | The proposed method performs better on multiple intent detection datasets and is stronger than existing methods. |
Similarity or deeper understanding? Analyzing the TED-Q dataset of evoked questions (2020.coling-main)
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| Challenge: | TED-Q datasets are annotated with the questions they implicitly evoke, based on a dataset of TED talks . we test whether relation between a discourse and questions it evokes is one of similarity or association . |
| Approach: | They construct a binary classification task from TED-Q and fit a BERT-based classifier alongside models based on different notions of similarity. |
| Outcome: | The proposed classifier outperforms similarity-based models in the TED-Q dataset. |
Sentiment Analysis for Emotional Speech Synthesis in a News Dialogue System (2020.coling-main)
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| Challenge: | In smart speakers and conversational robots, the demand for expressive speech synthesis has increased. |
| Approach: | They propose to annotate a news dataset with emotion labels for each sentence and to evaluate its effectiveness using the constructed dataset. |
| Outcome: | The proposed method improves the performance of the proposed model by preferentially annotating news articles with low confidence in the human-in-the-loop machine learning framework. |
Adversarial Learning on the Latent Space for Diverse Dialog Generation (2020.coling-main)
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| Challenge: | Existing methods for dialog generation generate generic utterances, e.g., always generating "I don't know" |
| Approach: | They propose a framework that uses generative adversarial nets to generate conditioned responses in dialogs. |
| Outcome: | The proposed model generates more fluent, relevant, and diverse responses than state-of-the-art methods. |
Inconsistencies in Crowdsourced Slot-Filling Annotations: A Typology and Identification Methods (2020.coling-main)
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| Challenge: | Standard slot-filling models train or finetune on large datasets of carefully-annotated data that is domain specific. |
| Approach: | They propose automatic methods to identify inconsistencies in crowd-annotated data . a slot-filling model can extract the tokens "New York" as a TO LOCATION slot in a query . |
| Outcome: | The proposed methods reveal inconsistencies in data, though there is scope for improvement. |
Online Versus Offline NMT Quality: An In-depth Analysis on English-German and German-English (2020.coling-main)
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Maha Elbayad, Michael Ustaszewski, Emmanuelle Esperança-Rodier, Francis Brunet-Manquat, Jakob Verbeek, Laurent Besacier
| Challenge: | Existing studies compare offline and online neural machine translation architectures . we examine the impact of online decoding constraints on the translation quality . |
| Approach: | They evaluate offline and online neural machine translation architectures using human evaluations on English-German and German-English language pairs. |
| Outcome: | The proposed models are particularly sensitive to latency constraints and are well-suited for offline translation tasks. |
Informative Manual Evaluation of Machine Translation Output (2020.coling-main)
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| Challenge: | a new method for manual evaluation of machine translation output is proposed . evaluators mark problematic parts of the translated text, not just overall scores . |
| Approach: | They propose a method for manual evaluation of machine translation output based on marking actual issues in the translated text. |
| Outcome: | The proposed method can be applied on any genre/domain and language pair . it can be guided by various types of quality criteria and can be used for other types of generated text. |
TransQuest: Translation Quality Estimation with Cross-lingual Transformers (2020.coling-main)
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| Challenge: | Recent advances in the field of sentence-level quality estimation (QE) are based on neural-based architectures that require resourceintensive training. |
| Approach: | They propose a framework for sentence-level quality estimation based on cross-lingual transformers and use it to implement and evaluate two different neural architectures. |
| Outcome: | The proposed framework outperforms open-source QE frameworks when trained on WMT datasets and is very competitive in transfer learning settings. |
Monolingual and Multilingual Reduction of Gender Bias in Contextualized Representations (2020.coling-main)
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| Challenge: | Prior work identifies a linear gender subspace and removes gender information by eliminating the subspace. |
| Approach: | They propose to use DensRay to obtain interpretable dense subspaces by applying it to attention heads and layers of BERT. |
| Outcome: | The proposed method performs on-par with prior approaches, but is more robust and preserves language model performance better. |
AraBench: Benchmarking Dialectal Arabic-English Machine Translation (2020.coling-main)
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| Challenge: | Existing efforts to translate Arabic dialects to English are limited due to the lack of evaluation benchmarks. |
| Approach: | They propose an evaluation suite for Arabic to English machine translation using existing Arabic resources. |
| Outcome: | The evaluation suite for Arabic to English machine translation is based on existing evaluation benchmarks. |
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks (2020.coling-main)
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| Challenge: | Pre-trained transformer models have shown great success in improving performance on downstream tasks, but fine-tuning on a new task still requires large amounts of labeled data. |
| Approach: | They propose a method which allows optimization-based meta-learning across tasks . they use transformers to train transformer models and find better generalizations . |
| Outcome: | The proposed method outperforms self-supervised training and pre-trained models on 17 NLP tasks. |
A Mixture-of-Experts Model for Learning Multi-Facet Entity Embeddings (2020.coling-main)
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| Challenge: | Existing methods for learning entity embeddings from text descriptions leave it to downstream applications to identify these different facets and to select the most relevant ones. |
| Approach: | They propose a model that instead learns several vectors for each entity, each of which captures a different aspect of the considered domain. |
| Outcome: | The proposed model learns several vectors for each entity, each of which intuitively captures a different aspect of the considered domain. |
Classifier Probes May Just Learn from Linear Context Features (2020.coling-main)
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| Challenge: | Current probing methods can help to better estimate the complexity of learning, but not build a foundation for speculations about the nature of the linguistic structure encoded in the learned representations. |
| Approach: | They propose to use token embeddings to test whether probing tasks contain linguistic structure . they argue that current probing methods do not provide enough information to support this hypothesis . |
| Outcome: | The proposed method can be scrutinized and proves that representations encode linguistic structure even without additional linguistic structures. |
Priorless Recurrent Networks Learn Curiously (2020.coling-main)
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| Challenge: | a recent study shows domain-general recurrent neural networks reproduce human language behaviours . a lack of a unified concept of number agreement between these processes is a limitation of the model . |
| Approach: | They propose to use domain-general recurrent neural networks without explicit linguistic inductive biases to reproduce human language behaviours. |
| Outcome: | The proposed model can learn number agreement within unnatural sentences, the authors show . they show that the model has an effective understanding of singular versus plural for individual sentences . |
Answer-driven Deep Question Generation based on Reinforcement Learning (2020.coling-main)
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| Challenge: | Existing methods for deep question generation focus on enhancing document representations, but little attention is paid to the answer information. |
| Approach: | They propose a deep question generation model that makes better use of the target answer as a guidance to facilitate question generation. |
| Outcome: | The proposed model outperforms state-of-the-art models in automatic and human evaluations on the hotpotQA dataset. |
Distinguishing Between Foreground and Background Events in News (2020.coling-main)
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| Challenge: | a new task is needed to distinguish between foreground and background events in news articles . |
| Approach: | They propose a task of distinguishing between foreground and background events in news articles . they also identify the general temporal position of background events relative to the foregoing period . |
| Outcome: | The proposed model achieves good performance on a dataset of news articles . |
Pick a Fight or Bite your Tongue: Investigation of Gender Differences in Idiomatic Language Usage (2020.coling-main)
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| Challenge: | Existing studies on gender-linked language have established foundations regarding cross-gender differences in lexical, emotional, and topical preferences, along with their sociological underpinnings. |
| Approach: | They compile a corpus of spontaneous linguistic productions annotated with speakers’ gender and perform an empirical study of gender differences in the usage of figurative language between male and female authors. |
| Outcome: | The results show that gender-specific idiomatic choices reflect gender-based lexical and semantic preferences in general language, men's and women's idioms express higher emotion than their literal language, and contextual analysis of idiomatic expressions reveals considerable differences, reflecting subtle divergences in usage environments, shaped by cross-gender communication styles and semantic biases. |
Intra-Correlation Encoding for Chinese Sentence Intention Matching (2020.coling-main)
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| Challenge: | Existing methods to improve sentence intention matching for Chinese text are limited due to the particularity of the text. |
| Approach: | They propose a method that combines character-granularity and word-granulularity features to perform sentence intention matching. |
| Outcome: | The proposed method can capture sentence feature information from multiple perspectives and correlation information between different levels of sentences. |
Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering (2020.coling-main)
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| Challenge: | Interpretability and explainability of deep neural net models are always challenging due to their size and complexity. |
| Approach: | They propose to design an explainable, evidence-based memory network architecture that connects current sample with seen samples and bases its decision on these samples. |
| Outcome: | The proposed model can trace errors to training instances that might have caused errors . the proposed model achieves state-of-the-art performance on two popular datasets . |
A Study on Efficiency, Accuracy and Document Structure for Answer Sentence Selection (2020.coling-main)
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| Challenge: | Existing approaches to QA re-rank sentences use huge neural models or complex attentive architectures. |
| Approach: | They propose to exploit the intrinsic structure of the original rank with an effective word-relatedness encoder to achieve the highest accuracy among the cost-efficient models. |
| Outcome: | The proposed model takes 9.5 seconds to train on the WikiQA dataset, compared with 18 minutes required by a standard BERT-base fine-tuning. |
Auto-Encoding Variational Bayes for Inferring Topics and Visualization (2020.coling-main)
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| Challenge: | Visualization and topic modeling are widely used for text analysis, but there is a lack of semantic interpretation as they do not extract topics. |
| Approach: | They propose a fast Auto-Encoding Variational Bayes based inference method for jointly inferring topics and visualization using a single generative model. |
| Outcome: | The proposed method can handle model changes efficiently with little rederivation effort. |
Hy-NLI: a Hybrid system for Natural Language Inference (2020.coling-main)
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| Challenge: | Recent work has revealed the generalization difficulties of deep models . however, such models fail to perform well on adversarial datasets with challenging linguistic phenomena. |
| Approach: | They propose a hybrid system that learns to identify an NLI pair as linguistically challenging or not . their system uses symbolic or deep learning components to make the final inference decision . |
| Outcome: | The proposed system outperforms existing models on adversarial datasets and on mainstream datasets. |
Identifying Motion Entities in Natural Language and A Case Study for Named Entity Recognition (2020.coling-main)
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| Challenge: | Identifying motion entities in text is not only challenging but beneficial for a better natural language understanding. |
| Approach: | They propose a Motion Entity Tagging model to identify entities in motion in a text using the Literal-Motion-in-Text dataset for training and evaluating the model. |
| Outcome: | The proposed method improves the Named-Entity Recognition task by splitting clauses and phrases from complex and long motion sentences. |
Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction (2020.coling-main)
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| Challenge: | Existing approaches to document-level relation extraction are difficult to establish direct connections between distant entity pairs. |
| Approach: | They propose a global context-enhanced Graph Convolutional Network model which captures rich global context information of entities in a document. |
| Outcome: | The proposed model captures rich global context information of entities in a document. |
Diverse Keyphrase Generation with Neural Unlikelihood Training (2020.coling-main)
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| Challenge: | Recent advances in neural natural language generation have made possible remarkable progress on the task of keyphrase generation, however, the importance of diversity in keyphrases has been largely ignored. |
| Approach: | They propose to train a sequence-to-sequence keyphrase generation model from the perspective of diversity. |
| Outcome: | The proposed model achieves large diversity gains while maintaining competitive output quality. |
User Memory Reasoning for Conversational Recommendation (2020.coling-main)
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| Challenge: | Existing systems that update user preferences via asking relevant questions are unable to dynamically maintain and reason over their knowledge for current (and possibly future) recommendations. |
| Approach: | They propose a new memory graph (MG) -> Conversational Recommendation parallel corpus with 7K+ human-to-human role-playing dialogs and a graph-based reasoning model that updates MG from unstructured utterances and predicts optimal dialog policies based on updated MG. |
| Outcome: | The proposed model is based on a large-scale user memory bootstrapped from real-world user scenarios and can be easily updated from unstructured utterances. |
Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs (2020.coling-main)
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| Challenge: | Community-based question answering platforms take time to get useful information from among many answers. |
| Approach: | They propose a method to select a diverse and non-redundant answer set rather than ranking the answers. |
| Outcome: | The proposed method outperforms baseline methods on a Japanese CQA site . it calculates the answer importance and similarity between answers by using BERT . |
An empirical analysis of existing systems and datasets toward general simple question answering (2020.coling-main)
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| Challenge: | evaluators of simple factoid question answering using different datasets are not able to solve SimpleQuestions. |
| Approach: | They evaluate the progress of the field toward solving simple factoid questions over a knowledge base. |
| Outcome: | The proposed model is nearly solved on the most popular dataset, but not on the robustness of existing systems. |
Learn to Combine Linguistic and Symbolic Information for Table-based Fact Verification (2020.coling-main)
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| Challenge: | Existing methods for fact verification lack attention to combine linguistic and symbolic information. |
| Approach: | They propose a graph-based reasoning approach that learns to combine linguistic and symbolic information effectively. |
| Outcome: | The proposed method can combine linguistic and symbolic information effectively. |
CosMo: Conditional Seq2Seq-based Mixture Model for Zero-Shot Commonsense Question Answering (2020.coling-main)
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| Challenge: | Identifying the implicit causes and effects of a social context is the driving capability of commonsense reasoning. |
| Approach: | They propose a conditional seq2seq-based mixture model which generates context-dependent clauses for commonsense reasoning. |
| Outcome: | The proposed model improves on the current state-of-the-art models by +5.2% over existing models. |
Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks (2020.coling-main)
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| Challenge: | Existing extractive summarization models hardly capture inter-sentence relationships, especially in long documents. |
| Approach: | They propose to use a graph neural network to capture inter-sentence relationships efficiently via graph-structured document representation. |
| Outcome: | The proposed model outperforms existing models on CNN/DM and NYT datasets and significantly outperfies them on longer documents. |
SaSAKE: Syntax and Semantics Aware Keyphrase Extraction from Research Papers (2020.coling-main)
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| Challenge: | Syntax and semantics Aware Keyphrase Extraction (SaSAKE) is a novel method for extracting keyphrases from research papers. |
| Approach: | They propose a method to automatically extract keyphrases from research papers using a transformer architecture and graph encoders to incorporate syntactic and semantic dependency graphs. |
| Outcome: | The proposed method achieves state-of-the-art performance on three benchmark datasets. |
News Editorials: Towards Summarizing Long Argumentative Texts (2020.coling-main)
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| Challenge: | Using news summarization, we aim to target opinionated articles with a well-defined argumentation structure. |
| Approach: | They present a corpus of carefully curated summaries for 266 news editorials. |
| Outcome: | The summarization of opinionated articles with a well-defined argumentation structure is evaluated using a tailored annotation scheme. |
Automatic Interlinear Glossing for Under-Resourced Languages Leveraging Translations (2020.coling-main)
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| Challenge: | Documentation is not a cure-all for language loss, but it is an important part of language preservation. |
| Approach: | They propose to use multi-source neural models to create automatic glossing models . they also explore cross-lingual transfer and a simple output length control mechanism . |
| Outcome: | The proposed model outperforms state-of-the-art models on low-resource scenarios. |
Scientific Keyphrase Identification and Classification by Pre-Trained Language Models Intermediate Task Transfer Learning (2020.coling-main)
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| Challenge: | Scientific keyphrase identification and classification is challenging due to lack of labeled data for training deep neural networks. |
| Approach: | They propose to use pre-trained language models BERT and SciBERT to train scientific keyphrase identification and classification using intermediate task transfer learning. |
| Outcome: | The proposed model achieves competitive performance in scientific keyphrase identification and classification compared to both previous studies and strong baselines. |
Exploiting Microblog Conversation Structures to Detect Rumors (2020.coling-main)
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| Challenge: | Existing models for rumor detection ignore the conversation structure of tweets . 68% of american adults occasionally read news on social media platforms . however, the credibility of news propagated through social media is questionable due to the lack of editors who can validate it. |
| Approach: | They propose to model Twitter conversation structure by modeling it as a graph to detect rumors by reading tweets that voice other users’ stances on the tweet. |
| Outcome: | The proposed model outperforms baseline models on two rumor datasets and shows that it outperformed several baseline models. |
Explainable Automated Fact-Checking: A Survey (2020.coling-main)
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| Challenge: | Steady progress has been made in fact-checking and its orthogonal tasks. |
| Approach: | They propose to use fact-checking explanations to explain predictions by comparing existing explanations against desirable properties to find out what makes for good explanations. |
| Outcome: | The proposed explanations are compared against desirable properties and show how they may lead to improvements in the research area. |
Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users based on Weakly Supervised Learning (2020.coling-main)
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| Challenge: | Existing models for fake news detection are often insufficient or lacking in features . a novel structure-aware multi-head attention network can detect fake news in 4 hours . |
| Approach: | They propose a structure-aware multi-head attention network to detect fake news in mass news . they use credibility of publishers and users as prior weakly supervised information . |
| Outcome: | The proposed model can detect fake news in 4 hours with an accuracy of over 91% . the proposed model is faster than the state-of-the-art models . |
Debunking Rumors on Twitter with Tree Transformer (2020.coling-main)
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| Challenge: | Existing methods for rumor detection follow tree edges or treat all posts fully-connected during feature learning. |
| Approach: | They propose a new rumor detection model based on tree transformer to better utilize user interactions in the dialogue . they propose to use post-level self-attention to aggregate the intra-/inter-subtree stances . |
| Outcome: | The proposed model improves rumor detection performance on social media conversations . it is based on a conversation tree that encodes important information indicative of credibility . |
Words are the Window to the Soul: Language-based User Representations for Fake News Detection (2020.coling-main)
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| Challenge: | Existing studies on fake news classification focus on textual content, but also social context in which news are consumed. |
| Approach: | They propose a model that creates representations of individuals on social media based only on the language they produce and uses them to detect fake news. |
| Outcome: | The proposed model exploits the relationship between language use and connections in the social graph to assess the presence of the Echo Chamber effect in the data. |
Argumentation Mining on Essays at Multi Scales (2020.coling-main)
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| Challenge: | Argumentation mining on essays is a new task in natural language processing. |
| Approach: | They propose a multi-scale argumentation mining model which aims to identify the types and locations of argumentation components from essay text. |
| Outcome: | The proposed model outperforms existing models on mining all types of argumentation components on the Persuasive Essay dataset. |
Data Augmentation for Multiclass Utterance Classification – A Systematic Study (2020.coling-main)
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| Challenge: | a lack of sufficient training data for some categories can cause imbalanced data distributions . a weak classifier may miscategorize a request, resulting in customer dissatisfaction . |
| Approach: | They propose to use random resampling, word-level transformations and neural text generation to augment existing data to cope with imbalanced data. |
| Outcome: | The proposed methods improve utterance classification results by drawing on utterant variation. |
KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi (2020.coling-main)
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| Challenge: | low-resource African languages are traditionally left behind because of the lack of well-annotated data and effective preprocessing. |
| Approach: | They propose two news datasets for multi-class classification of news articles in two low-resource African languages. |
| Outcome: | The proposed datasets show that training embeddings on the higher-resourced Kinyarwanda yields successful cross-lingual transfer to Kirundi. |
Go Simple and Pre-Train on Domain-Specific Corpora: On the Role of Training Data for Text Classification (2020.coling-main)
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| Challenge: | Pre-trained language models provide the foundations for state-of-the-art performance across a wide range of natural language processing tasks, including text classification. |
| Approach: | They compare the performance of a linear classifier based on word embeddings with a pre-trained language model, i.e., BERT, across a wide range of datasets and classification tasks. |
| Outcome: | The proposed method outperforms baselines in standard datasets with large training sets, but in settings with small training datasets it performs better. |
Unsupervised Fine-tuning for Text Clustering (2020.coling-main)
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| Challenge: | Existing approaches to text clustering fine-tune pre-trained models have been limited. |
| Approach: | They propose a method to fine-tune pre-trained models unsupervisedly for text clustering by learning text representations and cluster assignments using a clustering oriented loss. |
| Outcome: | The proposed model outperforms baseline methods and achieves state-of-the-art results on three text clustering datasets. |
Exploiting Narrative Context and A Priori Knowledge of Categories in Textual Emotion Classification (2020.coling-main)
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| Challenge: | Existing methods for recognizing the mental state of characters in text are limited by their use of character-specific contexts. |
| Approach: | They propose a method that encodes the preceding context of the target sentence along with the target phrase using a BERT-based text encoder. |
| Outcome: | The proposed method improves the accuracy of emotion classification by encoding the preceding context of the target sentence along with the target phrase using a BERT encoder. |
Word-Level Uncertainty Estimation for Black-Box Text Classifiers using RNNs (2020.coling-main)
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| Challenge: | Neural Networks are not interpretable, since they provide no information about why particular decisions were made. |
| Approach: | They propose to decompose and visualize uncertainty of text classifiers at the level of words to provide detailed explanations of uncertainties. |
| Outcome: | The proposed approach decomposes and visualizes uncertainty of text classifiers at the level of words and enables a deeper understanding of unreliable model behaviours. |
Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network (2020.coling-main)
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| Challenge: | a few-shot text classification method is proposed to solve the few-sshot text problem . supervised learning methods require large corpus of labeled data, making them hindered in practical application. |
| Approach: | They propose a few-shot text classification method that takes advantage of advanced pre-trained language models to extract the semantic features of each document. |
| Outcome: | The proposed method achieves state-of-the-art on sentiment analysis and relation datasets. |
ManyEnt: A Dataset for Few-shot Entity Typing (2020.coling-main)
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| Challenge: | ManyEnt is a benchmark for entity typing in few-shot scenarios . given a sentence with two entity mentions, the model is to assign the entity pair to a relation. |
| Approach: | They propose a benchmark for entity typing models in few-shot scenarios . they propose 256 entity types and 53 entity types for a fine-grain variant . |
| Outcome: | The proposed model can be used in few-shot scenarios with a fine-grain typeset and a coarse-grainability one with 53 entity types. |
Embedding Meta-Textual Information for Improved Learning to Rank (2020.coling-main)
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| Challenge: | a neural representation learning approach has not been extended to meta-textual information that is readily available for many IR tasks. |
| Approach: | They propose a framework that learns embeddings for meta-textual categories and optimizes a pairwise ranking objective for improved matching based on combined embedds of textual and meta-tactile information. |
| Outcome: | The proposed framework improves cross-lingual retrieval in the Wikipedia domain and Patent domain. |
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification (2020.coling-main)
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| Challenge: | Existing approaches to few-shot text classification require domain expertise and an understanding of the language model's abilities to define the mapping between words and labels. |
| Approach: | They propose a method that converts textual inputs to cloze questions that contain some form of task description and processes them with a pretrained language model to map the predicted words to labels. |
| Outcome: | The proposed approach performs almost as well as hand-crafted label-to-word mappings for a number of tasks with small amounts of training data. |
Knowledge Base Embedding By Cooperative Knowledge Distillation (2020.coling-main)
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| Challenge: | Knowledge bases are increasingly exploited as gold standard data sources for various knowledge-driven NLP tasks. |
| Approach: | They propose a method to perform knowledge base representation learning by mutually and jointly distilling knowledge within a dynamic teacher-student setting. |
| Outcome: | The proposed approach outperforms two baselines, traditional and sequential, on two standard datasets showing that it is possible to distill knowledge between KBs. |
IntKB: A Verifiable Interactive Framework for Knowledge Base Completion (2020.coling-main)
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| Challenge: | Knowledge bases (KBs) present databases that store information about entities and relations among them. |
| Approach: | They propose a question-based interactive framework for KB completion from text . their framework generates facts that are aligned with text snippets and is immediately verifiable by humans . |
| Outcome: | The proposed framework achieves a hit@1 ratio of 29.7% for initial unseen relations, and gradually improves to 46.2%. |
Reference and Document Aware Semantic Evaluation Methods for Korean Language Summarization (2020.coling-main)
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Dongyub Lee, Myeong Cheol Shin, Taesun Whang, Seungwoo Cho, Byeongil Ko, Daniel Lee, EungGyun Kim, Jaechoon Jo
| Challenge: | Existing methods for text summarization are based on recall-oriented understudy for gisting evaluation (ROUGE) scores do not reflect semantic meaning correspondences between generated and reference summaries. |
| Approach: | They propose to use Korean as a summarization language to generate a shorter form of text from the source document preserving salient information. |
| Outcome: | The proposed evaluation metrics improve the correlation between the metrics and human judgment. |
At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization (2020.coling-main)
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| Challenge: | Existing studies have shown that extracting sentences at sentence level is not the best solution for document summarization. |
| Approach: | They propose to extract sub-sentential units based on the constituency parsing tree and a neural extractive model which leverages the sub-sensential information and extracts them. |
| Outcome: | The proposed model performs competitively compared to full sentence extraction under automatic and human evaluations. |
Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT (2020.coling-main)
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| Challenge: | Existing extractive summarization models generate summaries by selecting salient sentences, but there is a gap between the human-written gold summary and oracle sentence labels. |
| Approach: | They propose to extract fact-level semantic units for better extractive summarization by incorporating a hierarchical structure into the model and incorporate it with BERT using a Hierarchical graph mask. |
| Outcome: | The proposed model achieves state-of-the-art on the CNN/DaliyMail dataset. |
Flight of the PEGASUS? Comparing Transformers on Few-shot and Zero-shot Multi-document Abstractive Summarization (2020.coling-main)
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| Challenge: | Recent work has shown that pre-trained transformers obtain remarkable performance on many natural language processing tasks including automatic summarization. |
| Approach: | They propose to use transformers to generate multi-document summarization where the summary is explicitly conditioned on a user-given topic statement or question. |
| Outcome: | The proposed models perform well on four challenging summarization datasets from the general domain and one from consumer health. |
WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization (2020.coling-main)
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| Challenge: | Existing methods to generate abstractive summarizations are lacking labeled training datasets. |
| Approach: | They propose a weakly supervised approach to generate a strong summary from a set of documents based on a query. |
| Outcome: | The proposed approach sets a new state-of-the-art in terms of evaluation metrics on the Document Understanding Conferences dataset. |
Multimodal Sentence Summarization via Multimodal Selective Encoding (2020.coling-main)
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| Challenge: | Existing methods for generating summary from text and image ignore that the image can improve the ability of the encoder to identify highlights of a news event or document. |
| Approach: | They propose a multimodal selective gate network that takes reciprocal relationships between textual and multi-level visual features into account to select highlights of the event. |
| Outcome: | The proposed model can generate summary for a given sentence-image pair using visual signals . it can also capture highlights embedded in the image more accurately, the authors show . |
Controllable Abstractive Sentence Summarization with Guiding Entities (2020.coling-main)
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| Challenge: | Existing text summarization models lack guiding entities to ensure that entities are present in summaries. |
| Approach: | They propose a controllable abstractive sentence summarization model which generates summaries with guiding entities. |
| Outcome: | The proposed model outperforms the state-of-the-art models in evaluation scores and informativeness metrics. |
HOLMS: Alternative Summary Evaluation with Large Language Models (2020.coling-main)
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| Challenge: | Efficient document summarization requires evaluation measures that can rank a set of systems based on an average score and highlight which individual summary is better than another. |
| Approach: | They propose a hybrid evaluation measure for document summarization called HOLMS that combines both language models pre-trained on large corpora and lexical similarity measures. |
| Outcome: | The proposed measure outperforms ROUGE and BLEU on several extractive summarization datasets for both linguistic quality and pyramid scores. |
How Domain Terminology Affects Meeting Summarization Performance (2020.coling-main)
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| Challenge: | Existing methods to summarize meetings using domain terminology are understudied . jargon terms are used to identify salient utterances from transcripts . |
| Approach: | They propose to use jargon terms to identify salient utterances from transcribed meetings to generate meeting minutes. |
| Outcome: | The proposed system generates minutes from transcribed meetings by identifying salient utterances from transcripts. |
An Anchor-Based Automatic Evaluation Metric for Document Summarization (2020.coling-main)
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| Challenge: | Existing reference-based evaluation metrics such as ROUGE have their own drawbacks. |
| Approach: | They propose a protocol for a reference-based automatic evaluation metric that requires the endorsement of source document. |
| Outcome: | The proposed metric is anchored on source document and has higher correlation with human judgments. |
Metrics also Disagree in the Low Scoring Range: Revisiting Summarization Evaluation Metrics (2020.coling-main)
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| Challenge: | In text summarization evaluation, evaluating the efficacy of automated metrics without human judgments has become popular. |
| Approach: | They revisit their experiments and find that automatic metrics disagree when ranking high-scoring summaries. |
| Outcome: | The proposed method is a human judgment-free method, but it is not a meta-evaluation strategy. |
On the Faithfulness for E-commerce Product Summarization (2020.coling-main)
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| Challenge: | e-commerce product summarization requires consistency between product attributes and summary . inconsistent product summaries can mislead users and decrease public credibility . |
| Approach: | They propose a model to generate e-commerce product summaries with product attributes . they encode product attribute table and constrain attribute words to be presented only through copying . |
| Outcome: | The proposed model significantly improves the faithfulness of e-commerce product summarization tasks. |
SumTitles: a Summarization Dataset with Low Extractiveness (2020.coling-main)
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| Challenge: | Existing methods for extractive summarization of dialogue data are limited by the grammar and structure of the utterances used. |
| Approach: | They propose a low-extractive corpus of movie dialogues for abstractive text summarization . they use an alignment algorithm to construct the corpus and a baseline evaluation . |
| Outcome: | The proposed method is low-extractive and shows high performance in dialogue datasets. |
TWEETSUM: Event oriented Social Summarization Dataset (2020.coling-main)
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| Challenge: | Developing social summarization systems is becoming more and more critical . but, the publicly available and high-quality large scale social summaries are rare . |
| Approach: | They propose to build a social summarization dataset using twitter's hot events . they collect user relations, hashtags and user profiles to evaluate their summarizing methods . |
| Outcome: | The proposed dataset is based on a dataset from twitter with 12 real world hot events with 44,034 tweets and 11,240 users. |
Exploiting a lexical resource for discourse connective disambiguation in German (2020.coling-main)
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| Challenge: | a connective lexicon can be a valuable resource for languages with limited PDTB-style annotations . connectives are usually understood to be ambiguous in two different ways . |
| Approach: | They propose to augment a purely-empirical approach to connective identification and sense classification in German . they find that a connective lexicon can be a valuable resource for those languages with a large PDTB-style-annotated coprus . |
| Outcome: | The proposed approach improves on published results and achieves an F1 score for German sense classification. |
Chinese Paragraph-level Discourse Parsing with Global Backward and Local Reverse Reading (2020.coling-main)
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| Challenge: | Existing methods on discourse parsing in English suffer from long discourse units and fewer explicit connectives. |
| Approach: | They propose to use two reading modes to construct Chinese paragraph level discourse trees. |
| Outcome: | The proposed model outperforms baselines on Chinese discourse trees. |
A Neural Model for Aggregating Coreference Annotation in Crowdsourcing (2020.coling-main)
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| Challenge: | Existing studies of natural language labelling tasks have shown that crowd-sourced labels can be noisy. |
| Approach: | They split the aggregation into mention classification and coreference chain inference tasks to predict the correct labels. |
| Outcome: | The proposed model predicts the class of each mention using an autoencoder while taking into account the mention’s annotation complexity and annotators’ reliability at different levels. |
Variation in Coreference Strategies across Genres and Production Media (2020.coling-main)
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| Challenge: | a lack of work on automatic coreference resolution on spoken and written language has led to inconclusive results. |
| Approach: | They propose to use Ontonotes, Switchboard and Twitter to investigate coreference . they find fairly clear patterns of "behavior" for the different genres/medias . |
| Outcome: | The results show that the choice of genre and the medium (spoken versus spoken) relates to the spokenwritten spectrum for coreference strategies. |
Towards automatically generating Questions under Discussion to link information and discourse structure (2020.coling-main)
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| Challenge: | Questions under Discussion (QUD) are emerging as a useful approach to spelling out the connection between information structure of sentences and nature of discourse. |
| Approach: | They propose a framework for QUD annotation based on explicit pragmatic principles . they propose generating all potentially relevant questions for a given sentence . |
| Outcome: | The proposed framework supports more reliable discourse structure annotation based on explicit questions . but the proposed approach is not robust enough for authentic data . |
Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training (2020.coling-main)
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| Challenge: | Existing few-shot relation classifiers struggle to distinguish them with few annotated instances due to high co-occurrence of some relations . |
| Approach: | They propose a few-shot relation classification model with two mechanisms to decouple easily-confused relations. |
| Outcome: | The proposed model achieves comparable and even better results to strong baselines in terms of accuracy. |
Semi-supervised Multi-task Learning for Multi-label Fine-grained Sexism Classification (2020.coling-main)
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| Challenge: | Sexism is a form of oppression based on one's sex and is reported online in numerous ways. |
| Approach: | They propose a multi-task approach for fine-grained multi-label sexism classification that leverages several supporting tasks without incurring manual labeling cost. |
| Outcome: | The proposed method outperforms the state-of-the-art for multi-label sexism classification on a recently released dataset across five standard metrics. |
Using Eye-tracking Data to Predict the Readability of Brazilian Portuguese Sentences in Single-task, Multi-task and Sequential Transfer Learning Approaches (2020.coling-main)
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Sidney Evaldo Leal, João Marcos Munguba Vieira, Erica dos Santos Rodrigues, Elisângela Nogueira Teixeira, Sandra Aluísio
| Challenge: | Sentence complexity assessment is a relatively new task in Natural Language Processing. |
| Approach: | They propose to use Brazilian Portuguese to evaluate sentences with linguistic features to improve readability. |
| Outcome: | The proposed model reaches the state-of-the-art for Brazilian Portuguese with 97.8% accuracy with linguistic features. |
Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework (2020.coling-main)
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| Challenge: | 65% of job descriptions miss describing a significant number of relevant skills, a problem we address with a deep learning model based on a multi-label classification problem . |
| Approach: | They propose a deep learning model to learn the set of enumerated job skills associated with a job description. |
| Outcome: | The proposed model improves on an existing baseline solution by over 9% and 7% absolute improvements in terms of recall and normalized discounted cumulative gain. |
Native-like Expression Identification by Contrasting Native and Proficient Second Language Speakers (2020.coling-main)
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| Challenge: | a novel task of native-like expression identification is proposed by contrasting texts written by native speakers and those by proficient second language speakers. |
| Approach: | They propose a task of native-like expression identification by contrasting texts written by native speakers and those by proficient second language speakers. |
| Outcome: | The proposed method uncovers linguistically interesting usages distinctive of native speech. |
An Analysis of Dataset Overlap on Winograd-Style Tasks (2020.coling-main)
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| Challenge: | a large number of test instances overlap considerably with pretraining corpora, a study finds . for a number of years, models struggled to exceed chance-level performance . |
| Approach: | They analyze the effects of varying degrees of overlaps that occur between pretraining corpora and test instances in WSC-style tasks. |
| Outcome: | The WSC-Web dataset is the largest to date and has lower overlaps with current pretraining corpora. |
The Indigenous Languages Technology project at NRC Canada: An empowerment-oriented approach to developing language software (2020.coling-main)
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Roland Kuhn, Fineen Davis, Alain Désilets, Eric Joanis, Anna Kazantseva, Rebecca Knowles, Patrick Littell, Delaney Lothian, Aidan Pine, Caroline Running Wolf, Eddie Santos, Darlene Stewart, Gilles Boulianne, Vishwa Gupta, Brian Maracle Owennatékha, Akwiratékha’ Martin, Christopher Cox, Marie-Odile Junker, Olivia Sammons, Delasie Torkornoo, Nathan Thanyehténhas Brinklow, Sara Child, Benoît Farley, David Huggins-Daines, Daisy Rosenblum, Heather Souter
| Challenge: | This paper describes the first, three-year phase of a project at the National Research Council of Canada that is developing software to assist Indigenous communities in preserving their languages and extending their use. |
| Approach: | They describe the first phase of a project at the National Research Council of Canada that is developing software to assist Indigenous communities in preserving their languages. |
| Outcome: | The proposed software will help Indigenous communities preserve and revitalize their languages and extend their use. |
Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings (2020.coling-main)
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| Challenge: | Emotion lexicons provide information about associations between words and emotions. |
| Approach: | They use crowdsourcing to annotate words with Plutchik's 8 basic emotions, providing binary labels. |
| Outcome: | The proposed lexicons provide information about associations between words and emotions . the lexiconics are useful in emotional analyses of reviews, literary texts, and posts on social media . |
Don’t Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities (2020.coling-main)
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| Challenge: | a new dataset is proposed to help develop NLP models to categorize language that is patronizing or condescending towards vulnerable communities. |
| Approach: | They propose to annotate a dataset to help develop NLP models to categorize language that is patronizing or condescending towards vulnerable communities. |
| Outcome: | The proposed dataset supports the development of NLP models to categorize language that is patronizing or condescending towards vulnerable communities. |
100,000 Podcasts: A Spoken English Document Corpus (2020.coling-main)
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Ann Clifton, Sravana Reddy, Yongze Yu, Aasish Pappu, Rezvaneh Rezapour, Hamed Bonab, Maria Eskevich, Gareth Jones, Jussi Karlgren, Ben Carterette, Rosie Jones
| Challenge: | Podcasts are a large and growing repository of spoken audio. |
| Approach: | They propose to use podcasts as a resource for speech processing and linguistics . they use a corpus of 100,000 podcasts to study the complexity of the domain . |
| Outcome: | The Spotify Podcast Dataset is the largest corpus of transcribed speech data . the dataset contains 60,000 hours of podcasts, with a range of genres and styles . |
A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment (2020.coling-main)
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| Challenge: | Existing methods for cross-lingual entity alignment ignore useful pre-aligned links between two KGs. |
| Approach: | They propose a novel method that jointly learns embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments. |
| Outcome: | The proposed method achieves remarkable performance gains on three benchmark cross-lingual entity alignment datasets. |
PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents (2020.coling-main)
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| Challenge: | Existing studies suggest that Neural Machine Translation still struggles with certain kinds of input with considerable noise, such as User-Generated Contents (UGC) on the Internet. |
| Approach: | They propose to evaluate the robustness of Neural Machine Translation models against specific linguistic phenomena in Japanese-English translation. |
| Outcome: | The proposed model can handle user-generated content (UGC) on the Internet, but it is difficult to translate clean inputs. |
Detecting Non-literal Translations by Fine-tuning Cross-lingual Pre-trained Language Models (2020.coling-main)
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| Challenge: | Non-literal translations are difficult to produce even for human translators, especially for foreign language learners, and machine translations have not yet been developed to simulate human translations. |
| Approach: | They propose to fine-tune generic sentence representations produced by a pre-trained cross-lingual language model to detect non-literal translations. |
| Outcome: | The proposed model can predict human translations and distinguish literal and non-literal translations at phrase level with a moderate positive correlation. |
WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking (2020.coling-main)
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| Challenge: | Using a neural reranking model, we can match a UMLS concept with a Wikipedia page, enabling manual alignment with minimal effort. |
| Approach: | They propose a cross-lingual neural reranking model to match a UMLS concept with a Wikipedia page, which achieves a recall@1of 72%, a substantial improvement of 20% over word- and char-level BM25. |
| Outcome: | The proposed model achieves recall@1of 72%, 20% better than word- and char-level BM25, and will facilitate easier access to Wikipedia for health professionals, patients, and NLP systems, including in multilingual settings. |
The Transference Architecture for Automatic Post-Editing (2020.coling-main)
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| Challenge: | A research challenge is the search for architectures that best support the capture, preparation and provision of src and mt information and its integration with pe decisions. |
| Approach: | They propose a multi-encoder based neural APE model that conditions post-editing decisions on both the source and machine translated text as inputs. |
| Outcome: | The proposed model outperforms the best performing systems by 1 BLEU point on the WMT 2016, 2017, and 2018 English–German APE shared tasks. |
Better Sign Language Translation with STMC-Transformer (2020.coling-main)
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| Challenge: | Current SLT approaches use a sign language recognition system to extract sign language glosses from videos. |
| Approach: | They propose to use a Sign Language Recognition system to extract sign language glosses from videos and a translation system to generate spoken language translations from the glossed sign language. |
| Outcome: | The proposed system outperforms existing methods on gloss-to-text and video-to text translations on the ASLG-PC12 corpus. |
A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction (2020.coling-main)
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| Challenge: | Recent work has questioned the robustness of unsupervised bilingual dictionary induction methods on distant language pairs. |
| Approach: | They propose an iterative dimension reduction method to bridge this gap . they propose a method that initializes and self-learning and inducing a dictionary . |
| Outcome: | The proposed method achieves 13.64 55.53% accuracy between English and four distant languages. |
Data Selection for Bilingual Lexicon Induction from Specialized Comparable Corpora (2020.coling-main)
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| Challenge: | Narrow specialized comparable corpora are small in size, making it difficult to build efficient models to acquire translation equivalents. |
| Approach: | They propose to use Tf-Idf and cross entropy to improve bilingual lexicon induction from specialized comparable corpora by a factor of 10 . |
| Outcome: | The proposed methods improve bilingual lexicon induction by a large margin. |
A Locally Linear Procedure for Word Translation (2020.coling-main)
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| Challenge: | Existing methods to learn word embeddings of two languages are limited by the expressiveness of the translation model. |
| Approach: | They propose an algorithm that uses multiple orthogonal translation matrices to model the mapping and derive an algorithm to learn these multiple matric. |
| Outcome: | The proposed algorithm achieves better performance in bilingual and cross-lingual word translation tasks compared to the single matrix baseline. |
Rethinking the Value of Transformer Components (2020.coling-main)
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| Challenge: | Empirical results show that certain components are more important than others . we propose a new training strategy that can improve Transformer models by distinguishing unimportant components . |
| Approach: | They propose a training strategy that distinguishes the unimportant components in training . they compare the impact of individual component (sub-layer) on model performance . |
| Outcome: | The proposed training strategy can improve translation performance by distinguishing unimportant components in training. |
The SADID Evaluation Datasets for Low-Resource Spoken Language Machine Translation of Arabic Dialects (2020.coling-main)
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| Challenge: | Low-resource Machine Translation (LRT) models are still lagging behind on low-resourced language pairs due to the scarcity of parallel training data. |
| Approach: | They introduce benchmark datasets for Arabic and its dialects to examine their properties . they bootstrap existing parallel sentences and complement this with multilingual training . |
| Outcome: | The proposed method bootstraps existing parallel sentences and complements multilingual training to achieve strong baselines. |
Combining Word Embeddings with Bilingual Orthography Embeddings for Bilingual Dictionary Induction (2020.coling-main)
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| Challenge: | Bilingual dictionary induction (BDI) is a task of finding target language translations of source language words. |
| Approach: | They propose to use bilingual orthography Embeddings to enrich BWE-based BDI with transliteration information to make a decision on which information source is more reliable for a particular word pair. |
| Outcome: | The proposed system improves on English-Russian BDI and shows that it can be built with only weak bilingual signals and even without any bilingual signal. |
Understanding Translationese in Multi-view Embedding Spaces (2020.coling-main)
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| Challenge: | Recent studies show footprints of the source language remain visible in translations . this is the first time departures from isomorphism between embedding spaces are used to track translationese. |
| Approach: | They exploit departures from isomorphism between spaces built from original target language and translations into this target language to predict relations between languages in an unsupervised way. |
| Outcome: | The proposed method exploits departures from isomorphism between embedding spaces to predict relations between languages in an unsupervised way. |
Building The First English-Brazilian Portuguese Corpus for Automatic Post-Editing (2020.coling-main)
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| Challenge: | Existing corpus for automatic post-editing of English and Brazilian Portuguese is limited. |
| Approach: | They introduce a corpus for Automatic Post-Editing of English and Brazilian Portuguese. |
| Outcome: | The proposed corpus improves on the English and Brazilian Portuguese languages. |
Analysing cross-lingual transfer in lemmatisation for Indian languages (2020.coling-main)
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| Challenge: | Inference-based scripts such as Abjad are difficult for cross-lingual models to learn in extremely low resource scenarios. |
| Approach: | They evaluate cross-lingual approaches for low resource languages and compare their performance against other models using different linguistic factors. |
| Outcome: | The proposed model on six low resource languages from two different families is compared with monolingual models on morphologically rich Indian languages. |
Neural Automated Essay Scoring Incorporating Handcrafted Features (2020.coling-main)
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| Challenge: | Automated essay scoring (AES) relies on handcrafted features, but recent studies have proposed a hybrid method that integrates handcrafted essay-level features into a DNN-AES model. |
| Approach: | They propose a method that integrates handcrafted features into a DNN-AES model. |
| Outcome: | The proposed method significantly improves the accuracy of existing methods. |
A Straightforward Approach to Narratologically Grounded Character Identification (2020.coling-main)
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| Challenge: | Existing definitions of character are based on simplified or implicit definitions that do not capture essential distinctions between characters and other referents in narratives. |
| Approach: | They propose a narratologically grounded definition of character that is based on clear narrological principles and annotated 170 narrative texts. |
| Outcome: | The proposed definition of character is based on clear narratological principles and can be reliably annotated (0.78 Cohen’s ). |
Fine-grained Information Status Classification Using Discourse Context-Aware BERT (2020.coling-main)
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| Challenge: | Existing work on fine-grained information status (IS) relies on many hand-crafted linguistic features. |
| Approach: | They propose a discourse context-aware BERT model for fine-grained IS classification . they show an improvement of 10.5 F1 points for bridging anaphora recognition . |
| Outcome: | The proposed model achieves 4.8 absolute accuracy improvement on ISNotes corpus compared to previous work on bridging anaphora recognition . |
Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution (2020.coling-main)
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| Challenge: | a limitation of coreference resolution models is the focus on single-antecedent anaphors. |
| Approach: | They propose a model for unrestricted resolution of split-antecedent anaphors with multiple antecedents . they use auxiliary corpora where split-antcedent ananaphor was annotated by crowd . |
| Outcome: | The proposed model significantly improves on a baseline enhanced by BERT embeddings on anaphoric reference corpus. |
How coherent are neural models of coherence? (2020.coling-main)
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| Challenge: | Existing approaches to model coherence are limited to small newswire corpora . evaluators need to be trained on lexical and document levels to perform evaluations . |
| Approach: | They propose four generic evaluation tasks that capture coherence-specific properties . they aim at capturing correct use of discourse connectives and lexical cohesion . |
| Outcome: | The proposed tasks capture coherence-specific properties, including correct use of discourse connectives, lexical cohesion, temporal consistency among events and participants in a story. |
Fact vs. Opinion: the Role of Argumentation Features in News Classification (2020.coling-main)
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| Challenge: | A 2018 study led by the Media Insight Project showed that most journalists think that their news organizations should clearly mark what is news reporting and what is commentary or opinion in order to combat fake news and gain public trust. |
| Approach: | They propose to classify news articles into newsstories and opinion pieces using models that aim to sup-plement the article content representation with argumentation features. |
| Outcome: | The proposed model outperforms linguistic features and improves on fine-tuned transformer-based models on data from publishers. |
Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification (2020.coling-main)
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| Challenge: | Existing methods for generating textual-based explanations are highly implausible and damage a user’s trust in the automated system. |
| Approach: | They propose a method which first applies robust transformer models on a real-world, up-to-date, self-collected mergers and acquisitions dataset and then generates plausible, post-hoc, counterfactual explanations. |
| Outcome: | The proposed model improves model accuracy and human performance while generating plausible explanations based on human trials. |
Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer’s Disease Detection (2020.coling-main)
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| Challenge: | Existing methods for AD detection are too expensive and time-consuming to cover all potential patients. |
| Approach: | They propose a contrastive learning method to obtain effective text representations based on monolingual embeddings of BERT and a cross-lingual data augmentation method by building autoencoders to learn the text representation shared by both languages. |
| Outcome: | The proposed method outperforms other methods on a Mandarin AD corpus and achieves 81.6% detection accuracy. |
Multilingual Epidemiological Text Classification: A Comparative Study (2020.coling-main)
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| Challenge: | a comparative study of multilingual text classification models analyzes the performance of different models based on different languages . low-resource languages are highly influenced by typology of the languages on which the models have been trained or fine-tuned but also by their size. |
| Approach: | They compare machine and deep learning models with a dataset of epidemiological news articles . they find that the performance of the models is proportionate to the training data size . |
| Outcome: | The proposed model outperforms baseline models on a multilingual text classification task . low-resource languages are highly influenced by typology of languages and their size . |
Pointing to Select: A Fast Pointer-LSTM for Long Text Classification (2020.coling-main)
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| Challenge: | Existing methods to skip irrelevant words in text processing are slow and vanishing gradients can cause slow inference and a loss of coherence. |
| Approach: | They propose a pointer network-based LSTM framework which can change skip rates during inference. |
| Outcome: | The proposed model is 1.1x3.5x faster than the standard LSTM framework and more accurate than Leap-LSTM at high skip rates. |
Aspect-based Document Similarity for Research Papers (2020.coling-main)
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| Challenge: | Traditional document similarity measures do not consider in what aspects two documents are similar. |
| Approach: | They extend document similarity with aspect information by performing a pairwise document classification task. |
| Outcome: | The proposed approach is best performing on 172,073 research paper pairs from the ACL Anthology and CORD-19 corpus. |
Explainable and Sparse Representations of Academic Articles for Knowledge Exploration (2020.coling-main)
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| Challenge: | a system for summarizing academic articles by concept tagging has shown great coverage and high accuracy of concept identification. |
| Approach: | They propose to transform tagged concepts into sparse vectors as representations of academic documents. |
| Outcome: | The proposed system can be applied to a broader class of applications. |
“What is on your mind?” Automated Scoring of Mindreading in Childhood and Early Adolescence (2020.coling-main)
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Venelin Kovatchev, Phillip Smith, Mark Lee, Imogen Grumley Traynor, Irene Luque Aguilera, Rory Devine
| Challenge: | Existing studies show that children who excel at mindreading are more likely to be identified as popular by classmates and have reciprocated friendships. |
| Approach: | They propose to automate the scoring of mindreading ability in middle childhood and early adolescence using a new corpus of 11,311 question-answer pairs in English from 1,066 children aged from 7 to 14 . |
| Outcome: | The proposed scoring system is based on 11,311 question-answer pairs in English from 1,066 children aged from 7 to 14 . the results demonstrate the applicability of state-of-the-art NLP solutions to a new domain and task. |
A Deep Metric Learning Method for Biomedical Passage Retrieval (2020.coling-main)
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| Challenge: | Existing methods for passage retrieval are based on metric learning . the proposed approach is particularly well suited for domain-specific passage retrievals where it is very important to take into account different sources of information. |
| Approach: | They propose a method that learns a metric for questions and passages based on their internal semantic interactions. |
| Outcome: | The proposed method outperforms triplet loss and state-of-the-art methods in a biomedical passage retrieval task and outperformed triplet losses by 10% and 26%. |
Hierarchical Text Segmentation for Medieval Manuscripts (2020.coling-main)
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| Challenge: | Until now, text segmentation methods have only addressed data sets lying within the scope of narrative and expository texts or user dialogues texts. |
| Approach: | They propose a bottom-up greedy approach that enhances the results . they argue that books of hours exhibit a complex hierarchical entangled structure . |
| Outcome: | The proposed bottom-up greedy approach significantly enhances the results. |
Are We Ready for this Disaster? Towards Location Mention Recognition from Crisis Tweets (2020.coling-main)
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| Challenge: | Despite the widespread use of Twitter during emergencies, the majority of tweets do not have geoinformation. |
| Approach: | They propose to use Twitter to train location mention recognition models using different training settings. |
| Outcome: | The results show that training on near or far-away events boosts the performance compared to training on distant events. |
Mining Crowdsourcing Problems from Discussion Forums of Workers (2020.coling-main)
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| Challenge: | Among the most widely used platforms are Upwork, Appen, and above all Amazon Mechanical Turk (MTurk) which host annotation tasks and collect huge sets of annotated data from workers. |
| Approach: | They propose to use topic modeling to analyze workers' complaints from a new English corpus of workers’ forum discussions to identify problems in task design, task operation, and task evaluation that workers face with requesters in crowdsourcing processes. |
| Outcome: | The findings form the basis for future research on how to improve crowdsourcing processes. |
AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts (2020.coling-main)
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Mohit Chandra, Ashwin Pathak, Eesha Dutta, Paryul Jain, Manish Gupta, Manish Shrivastava, Ponnurangam Kumaraguru
| Challenge: | Existing studies on estimating the severity of abuse and the target of online abuse have focused on detecting and curtailment of such types of abuse. |
| Approach: | They propose to analyze online abuse from the perspective of presence, severity and target of abusive behavior from 7,601 posts from Gab and to estimate the severity of abuse. |
| Outcome: | The proposed system achieves 80% accuracy for abuse presence, 82% accuracy for abusive target prediction, and 65% accuracy for severity prediction. |
A Survey of Automatic Personality Detection from Texts (2020.coling-main)
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| Challenge: | Personality profiling has long been used in psychology to predict life outcomes. |
| Approach: | They present the trajectory of automatic personality detection from purely psychology approaches to the latest purely natural language processing approaches on large social media datasets. |
| Outcome: | The proposed models have been compared with the most recent approaches on large social media datasets. |
Mama/Papa, Is this Text for Me? (2020.coling-main)
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Rashedur Rahman, Gwénolé Lecorvé, Aline Étienne, Delphine Battistelli, Nicolas Béchet, Jonathan Chevelu
| Challenge: | Existing methods to predict minimal age from which text can be understood for children are unresolved in computational linguistics. |
| Approach: | They propose a method which predicts the minimum age from which a text can be understood by a recurrent neural network. |
| Outcome: | The proposed method outperforms state-of-the-art models at sentence and text levels and achieves mean absolute errors of 1.86 and 2.28. |
Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation (2020.coling-main)
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| Challenge: | Existing methods for review rating prediction ignore hierarchies among data . paper review rating predictions are important for improving paper review process . |
| Approach: | They propose a Hierarchical bi-directional self-attention Network framework for paper review rating prediction and recommendation . they leverage hierarchical structure of paper reviews with three levels of encoders . |
| Outcome: | The proposed approach can be used to make an effective decision-making tool for the academic paper review process. |
Context in Informational Bias Detection (2020.coling-main)
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| Challenge: | Informational bias is conveyed through sentences or clauses that provide tangential, speculative or background information that can sway readers’ opinions towards entities. |
| Approach: | They explore four kinds of context for informational bias in English news articles . integrating event context improves classification performance over a strong baseline . |
| Outcome: | The best-performing model outperforms the baseline on longer sentences and sentences from politically centrist articles. |
HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection (2020.coling-main)
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| Challenge: | Existing methods to detect online hate speech depend heavily on labeled datasets for training, which results in poor detection performance of the hate speech class. |
| Approach: | They propose a deep generative reinforcement learning model which augments two commonly-used hate speech detection datasets with the HateGAN generated tweets. |
| Outcome: | The proposed model improves the detection performance of hate speech class regardless of the classifiers and datasets used in the detection task. |
Evaluating Unsupervised Representation Learning for Detecting Stances of Fake News (2020.coling-main)
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| Challenge: | Using unsupervised representation learning, automated Fake News detection is a challenge for researchers. |
| Approach: | They examine pre-trained language models with respect to their performance on two Fake News related data sets. |
| Outcome: | The proposed models outperform the autoregression-based models on two Fake News related data sets. |
XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages (2020.coling-main)
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| Challenge: | XHate-999 is a multi-domain and multilingual evaluation data set for abusive language detection . we show that domain- and language-adaption can lead to substantially improved abusive language detecting in the target language . |
| Approach: | They propose a multi-domain and multilingual evaluation data set for abusive language detection that allows for disentanglement of domain transfer and language transfer effects. |
| Outcome: | The proposed model can significantly improve abusive language detection in the target language in the zero-shot transfer setups. |
Detect All Abuse! Toward Universal Abusive Language Detection Models (2020.coling-main)
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| Challenge: | Existing work on online abusive language detection focused on detecting a single abusive language problem in a domain, like Twitter, but none of them was successfully transferable to general ALD in different online communities. |
| Approach: | They propose a generic ALD framework that can address multiple types of ALD tasks across different domains and use a textual graph embedding to analyse the user’s linguistic behaviour. |
| Outcome: | The proposed framework surpasses the current state-of-the-art ALD algorithms across seven datasets covering multiple aspects of abusive language and different online community domains. |
Modeling Evolution of Message Interaction for Rumor Resolution (2020.coling-main)
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| Challenge: | Existing methods for rumor resolution ignore local interactions during the message diffusion which is important for the identification of rumors. |
| Approach: | They propose to model confrontation and reciprocity between message pairs via discrete variational autoencoders which effectively reflects the diversified opinion interactivity. |
| Outcome: | Experiments on a PHEME dataset show that the proposed model achieves higher accuracy than existing methods. |
Regularized Attentive Capsule Network for Overlapped Relation Extraction (2020.coling-main)
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| Challenge: | Existing methods to extract relations from distant supervision contain low-quality instances with noisy words and overlapped relations. |
| Approach: | They propose a Regularized Attentive Capsule Network to better identify overlapped relations in informal sentences . they embed multi-head attention into the capsule network as the low-level capsules . |
| Outcome: | Extensive experiments show that the proposed model improves relation extraction. |
Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction (2020.coling-main)
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| Challenge: | Existing approaches to supervised relational triple extraction require huge amounts of labeled data. |
| Approach: | They propose a multi-prototype embedding network model to extract the composition of relational triples from unstructured text. |
| Outcome: | The proposed method improves the performance of the few-shot relational triple extraction problem. |
Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation (2020.coling-main)
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| Challenge: | Existing studies on relation extraction (RE) use labeled training data for relation extraction models but it is expensive and time-consuming. |
| Approach: | They propose a dual supervision framework which utilizes both types of data to train relation extraction models. |
| Outcome: | The proposed framework can predict labels by human annotation and distant supervision without labeling bias since it is expensive and time-consuming. |
Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction (2020.coling-main)
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| Challenge: | Existing graph convolutional networks use pruned dependency trees to filter irrelevant nodes from sentence graphs. |
| Approach: | They propose to construct multiple sub-graphs from shortest dependency path and words linked to entities in the dependency parse to obtain more informative features useful for relation extraction. |
| Outcome: | The proposed method achieves state-of-the-art performance on a sentence-level relation extraction dataset and the SemEval 2010 Task 8 sentence- level relation extraction data. |
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction (2020.coling-main)
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| Challenge: | Distant Supervision (DS) generates large-scale annotated data but has wrong labels that result in incorrect evaluation scores during testing. |
| Approach: | They build a dataset using DS-generated data as training data and hire annotators to label test data. |
| Outcome: | The proposed dataset NYTH has a much larger test set and performs more accurate and consistent evaluation. |
Multi-choice Relational Reasoning for Machine Reading Comprehension (2020.coling-main)
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| Challenge: | cloze-style reading comprehension is a task that requires much semantic understanding and reasoning using various clues from texts. |
| Approach: | They propose a multi-choice relational reasoning model that emulates human reading comprehension by combining fusion representations of document, query and candidates. |
| Outcome: | The proposed model outperforms baseline models significantly on four datasets. |
FASTMATCH: Accelerating the Inference of BERT-based Text Matching (2020.coling-main)
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| Challenge: | Recent pre-trained language models have shown state-of-the-art accuracies in text matching. |
| Approach: | They propose a BERT-based text matching model where representations and interactions are decoupled . they propose generating final matching scores using a lightweight attention network . |
| Outcome: | Experiments show that the proposed model can achieve up to 100X speed-up to BERT and RoBERTa while keeping more up to 98.7% of the performance. |
DT-QDC: A Dataset for Question Comprehension in Online Test (2020.coling-main)
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| Challenge: | Existing methods to measure difficulty of questions are not accurate enough to guide learning. |
| Approach: | They propose to use a Chinese DT-QDC dataset to measure difficulty of questions and provide a new model that can improve the judgment of difficulty from different perspectives. |
| Outcome: | The proposed methods outperform baselines by 7.79% on F1-score and 15.92% on MAE, 28.26% on MSE, and 28.2% on MSC on the new DT-QDC dataset. |
Read and Reason with MuSeRC and RuCoS: Datasets for Machine Reading Comprehension for Russian (2020.coling-main)
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| Challenge: | MRC in other languages, including Russian, has not been well-addressed due to the lack of high-quality and large-scale datasets. |
| Approach: | They propose two Russian machine reading comprehension datasets that require reasoning over multiple sentences and commonsense knowledge to infer the answer. |
| Outcome: | The proposed datasets are more complex than the original ones for Russian . the results show that the proposed models are challenging for advanced models . |
Knowledge-Enhanced Natural Language Inference Based on Knowledge Graphs (2020.coling-main)
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| Challenge: | Existing approaches to natural language inference rely on semantic knowledge, but background knowledge is limited to a few specific types. |
| Approach: | They propose a Knowledge Graph-enhanced NLI model that leverages background knowledge stored in knowledge graphs to facilitate inference. |
| Outcome: | The proposed model can leverage background knowledge stored in knowledge graphs to perform the task. |
NYTWIT: A Dataset of Novel Words in the New York Times (2020.coling-main)
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| Challenge: | Novel words, or Out-Of-Vocabulary words, are a pervasive problem in modern natural language processing. |
| Approach: | They present a dataset of over 2,500 novel English words published in the New York Times . they use uncontextual and contextual predictions to predict novelty class . |
| Outcome: | The proposed dataset includes over 2,500 novel English words published in the New York Times between November 2017 and March 2019 . baseline results show that there is room for improvement even for state-of-the-art NLP systems . |
SOME: Reference-less Sub-Metrics Optimized for Manual Evaluations of Grammatical Error Correction (2020.coling-main)
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| Challenge: | Existing reference-less metrics are not optimized for manual evaluations of system outputs because no dataset exists for manual analysis. |
| Approach: | They propose a reference-less metric trained on manual evaluations of system outputs for grammatical error correction. |
| Outcome: | The proposed metric improves correlation with manual evaluation in system- and sentence-level meta-evaluation. |
Continual Lifelong Learning in Natural Language Processing: A Survey (2020.coling-main)
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| Challenge: | Existing approaches to continual learning (CL) are costly and time-consuming. |
| Approach: | They propose to examine the problem of continual learning in NLP through the lens of various NLP tasks and provide a critical review of existing methods. |
| Outcome: | The proposed methods are critical to the development of CL models and provide a critical review of existing methods and datasets. |
XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection (2020.coling-main)
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| Challenge: | XED is a multilingual fine-grained emotion dataset for English and other low-resource languages. |
| Approach: | They propose a multilingual fine-grained emotion dataset using Plutchik's Wheel of Emotions and a projection scheme to annotate Finnish and English sentences. |
| Outcome: | The proposed dataset is based on human-annotated Finnish and English sentences and projected annotations for 30 additional languages. |
Human or Neural Translation? (2020.coling-main)
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Shivendra Bhardwaj, David Alfonso Hermelo, Phillippe Langlais, Gabriel Bernier-Colborne, Cyril Goutte, Michel Simard
| Challenge: | a recent study shows that deep neural models have improved machine translation . identifying machine translation is still feasible, but is not yet known. |
| Approach: | They train and apply deep neural models to distinguish between human and machine translations . they use a monolingual and bilingual task to train and train 18 classifiers based on their results . |
| Outcome: | The proposed model improves the ability to distinguish between human and machine translations at the sentence level. |
Biomedical Concept Relatedness – A large EHR-based benchmark (2020.coling-main)
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| Challenge: | Existing biomedical concept relatedness datasets are notoriously small and consist of hand-picked concept pairs. |
| Approach: | They propose to use a concept relatedness benchmark to test the suitability of AI in healthcare . they find that it is six times larger than existing concepts relatedness datasets . |
| Outcome: | The proposed benchmark is six times larger than existing biomedical concept relatedness datasets and is relevant for the application of interest. |
Domain-Specific Sentiment Lexicons Induced from Labeled Documents (2020.coling-main)
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| Challenge: | Existing sentiment lexicons reflect abstract notion of polarity and do not do justice to substantial differences of word polarities between domains. |
| Approach: | They propose to use domain-specific sentiment lexicons to induce initial word intensity scores and train new deep models based on word vector representations to overcome the scarcity of the seed data. |
| Outcome: | The proposed models show that they perform well on review classification and cross-lingual word sentiment prediction. |
Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus (2020.coling-main)
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| Challenge: | Large text corpora are increasingly important for a wide variety of NLP tasks. |
| Approach: | They propose to train automatic language identification models on up to 1,629 languages . they find that human-judged accuracy for web-crawl text corpora is only around 5% for many lower-resource languages. |
| Outcome: | The proposed models achieve over 90% average F1 on 1,629 languages . human-judged accuracy for web-crawl text corpora is only around 5% for many lower-resource languages - suggesting a need for more robust evaluation. |
Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps (2020.coling-main)
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| Challenge: | Existing multi-hop question answering datasets do not provide a complete explanation for the reasoning process from the question to the answer. |
| Approach: | They propose a multi-hop question answering dataset that uses structured and unstructured data to test reasoning skills. |
| Outcome: | The proposed dataset ensures multi-hop reasoning while being challenging for multi-models. |
RoBERT – A Romanian BERT Model (2020.coling-main)
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| Challenge: | Existing pre-trained language models learn contextualized representations by using unlabeled text data and obtain state of the art results on a multitude of NLP tasks. |
| Approach: | They propose a pre-trained BERT model for Romanian language processing and compare it with multi-lingual models on seven Romanian specific NLP tasks. |
| Outcome: | The proposed model outperforms multi-lingual models on seven Romanian specific NLP tasks on sentiment analysis, dialect and cross-dialect topic identification, and diacritics restoration. |
Exploring the Language of Data (2020.coling-main)
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| Challenge: | Structured data, such as database tables or XML trees, often contain short natural language labels that describe the data structure itself or provide content (attribute values). Conventional NLP tools, such supervised sequence labellers or embeddings trained on full sentences, do not perform well on structured data. |
| Approach: | They propose to design a type of abbreviated grammar that is called the Language of Data and to investigate the grammatical properties of such labels. |
| Outcome: | The proposed model outperforms models trained on standard text on tokenisation, part-of-speech tagging, and named entity recognition over real-world structured data. |
DaN+: Danish Nested Named Entities and Lexical Normalization (2020.coling-main)
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| Challenge: | Named Entity Recognition (NER) is a task of finding entities in text, such as locations, organizations, and persons. |
| Approach: | They propose a multi-domain corpus and annotation guidelines for Danish nested named entities and lexical normalization to support research on cross-lingual cross-domain learning for a less-resourced language. |
| Outcome: | The proposed model outperforms existing models on the Danish Named Entity Recognition task and shows that it is robust to domain shifts and is highly effective on the least canonical data. |
New Benchmark Corpus and Models for Fine-grained Event Classification: To BERT or not to BERT? (2020.coling-main)
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| Challenge: | Existing methods for fine-grained event classification are of tiny size, ranging from 5-10K events. |
| Approach: | They propose to use ACLED data for fine-grained event classification . they compare performance of various state-of-the-art models on these datasets . |
| Outcome: | The proposed models perform better on micro (94.3-94.9%) and macro F1 (86.0-88.9%) the proposed models are robust and the performance is dependent on training data size. |
A Geometry-Inspired Attack for Generating Natural Language Adversarial Examples (2020.coling-main)
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| Challenge: | Existing techniques to generate adversarial examples for natural language are limited . previous research on adversarials focused on images, but this is not the case with natural language. |
| Approach: | They propose a geometry-inspired attack for generating natural language adversarial examples . they use deep neural networks to iteratively approximate the decision boundary . |
| Outcome: | The proposed attack fools natural language models with high success rates while replacing a few words. |
Expert Concept-Modeling Ground Truth Construction for Word Embeddings Evaluation in Concept-Focused Domains (2020.coling-main)
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| Challenge: | a novel method for concept-modeling ground truths is developed to evaluate the application of word embeddings in concept-focused textual domains. |
| Approach: | They propose a domain expert-controlled procedure for the construction of concept-modeling ground truths with the aim of evaluating the application of word embeddings. |
| Outcome: | The proposed method evaluates the application of word embeddings in concept-focused textual domains. |
Creation of Corpus and analysis in Code-Mixed Kannada-English Twitter data for Emotion Prediction (2020.coling-main)
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| Challenge: | Existing work on emotion prediction for resource-rich languages has focused on code-mixed social media corpus but not on Kannada-English code-mixed Twitter data. |
| Approach: | They analyze Kannada-English code-mixed Twitter corpus annotated with their respective ‘Emotion’ for each tweet. |
| Outcome: | The proposed model based on Kannada-English code-mixed Twitter corpus yielded an accuracy of 30% and 32% respectively. |
Fair Evaluation in Concept Normalization: a Large-scale Comparative Analysis for BERT-based Models (2020.coling-main)
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| Challenge: | a large number of biomedical entity mentions are retrieved from different ontologies, requiring non-syntactic interpretation. |
| Approach: | They propose to use bidirectional encoder representations from transformers to link biomedical entities across three domains for a task called medical concept normalization. |
| Outcome: | The proposed neural architectures are efficient for linking biomedical entities across domains and corpora. |
A Sentence Cloze Dataset for Chinese Machine Reading Comprehension (2020.coling-main)
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| Challenge: | Using cloze-style reading comprehension, Chinese machine reading comprehension datasets are becoming more and more popular . a new task is proposed to fill the right candidate sentence into the passage with several blanks . |
| Approach: | They propose a Chinese task to fill the right candidate sentence into a passage with blanks . they build a dataset to evaluate the difficulty of the task and make fake candidates . |
| Outcome: | The proposed task fills the right candidate sentence into the passage with blanks . the proposed dataset contains over 100K blanks within over 10K passages based on Chinese narrative stories . |
Improving Document-Level Sentiment Analysis with User and Product Context (2020.coling-main)
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| Challenge: | Existing work that improves document-level sentiment analysis by encoding user and product information has been limited to considering only the text of the current review. |
| Approach: | They propose to incorporate all available historical review text belonging to the author of the review in question and investigate the inclusion of his- torical reviews associated with the current product. |
| Outcome: | The proposed model improves on IMDB, Yelp 2013 and Yelpan 2014 datasets by more than 2 percentage points in the best case. |
Multilingual Neural RST Discourse Parsing (2020.coling-main)
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| Challenge: | Existing studies on text discourse parsing for English are limited due to the lack of annotated data. |
| Approach: | They propose to use multilingual vector representations and segment-level translation to establish a neural, cross-lingual discourse parser. |
| Outcome: | The proposed model achieves state-of-the-art on cross-lingual, document-level discourse parsing on all sub-tasks. |
Intrinsic Quality Assessment of Arguments (2020.coling-main)
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| Challenge: | Several quality dimensions of natural language arguments have been investigated. |
| Approach: | They propose to use a computational method to assess 15 quality dimensions of arguments by learning only from an argument's text. |
| Outcome: | The proposed approach achieves moderate but significant learning success for most dimensions. |
Tree Representations in Transition System for RST Parsing (2020.coling-main)
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| Challenge: | Existing studies have proposed a series of actions to build a right-heavy binarized tree for RST parsing, but the nodes of the binary-nuclear relations have the same nuclear type as those of the multi-nullar relations. |
| Approach: | They propose a nuclear type for multi-nuclear relations and a new action to construct a multi-branch tree. |
| Outcome: | The proposed nuclear type and action are more capable of capturing multi-nuclear relation and the joint action is more suitable than the separate one. |
Incremental Neural Lexical Coherence Modeling (2020.coling-main)
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| Challenge: | Recent work on pretrained language models has led to significant improvements in a range of NLP tasks. |
| Approach: | They propose a coherence model which interprets sentences incrementally to capture lexical relations between them. |
| Outcome: | The proposed model interprets sentences incrementally to capture lexical relations between them. |
Statistical Parsing of Tree Wrapping Grammars (2020.coling-main)
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| Challenge: | Using tree-wrapping grammars, we propose a statistical parsing algorithm for the grammar. |
| Approach: | They propose a statistical parsing algorithm based on neural supertagging and A* parse for Tree-Wrapping Grammars (TWG) they extract a grammar for English from constituency treebanks and discuss first parser results with this grammar. |
| Outcome: | The proposed algorithm is based on neural supertagging and A* parsing. |
Out-of-Task Training for Dialog State Tracking Models (2020.coling-main)
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Michael Heck, Christian Geishauser, Hsien-chin Lin, Nurul Lubis, Marco Moresi, Carel van Niekerk, Milica Gasic
| Challenge: | Dialog state tracking (DST) suffers from data sparsity. |
| Approach: | They utilize non-dialog data from unrelated NLP tasks to train dialog state trackers . they propose to use dialog state tracking to summarise the conversation history . |
| Outcome: | The proposed method exploits non-dialog data from unrelated NLP tasks to train dialog state trackers. |
Resource Constrained Dialog Policy Learning Via Differentiable Inductive Logic Programming (2020.coling-main)
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| Challenge: | Existing methods for dialog policy learning have limited data collection and data analysis. |
| Approach: | They introduce dialog policy learning via differentiable inductive logic on SimDial and MultiWoZ to address resource constrained dialog policy. |
| Outcome: | The proposed method is 100x more data efficient than state-of-the-art neural approaches on MultiWoZ while achieving similar performance metrics. |
German’s Next Language Model (2020.coling-main)
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| Challenge: | In this paper we compare the performance of our deep transformer based language models to existing models. |
| Approach: | They present a set of BERT and ELECTRA based German language models, GBERT and GELECTRE. |
| Outcome: | The proposed models outperform the previous best models on NER and classification tasks but are prohibitively large for many. |
Language Model Transformers as Evaluators for Open-domain Dialogues (2020.coling-main)
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| Challenge: | Computer-based systems for communication with humans are a cornerstone of AI research since the 1950s. |
| Approach: | They propose to use transformer neural networks to predict one or more words based on an already given context to provide an efficient, automatic indication of dialogue quality. |
| Outcome: | The proposed language models show that human evaluators have a positive correlation between the output of the models and scores. |
Embedding Dynamic Attributed Networks by Modeling the Evolution Processes (2020.coling-main)
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| Challenge: | Existing methods to embed nodes into low-dimensional vectors focus on static networks, but in practice, many networks are evolving over time and hence are dynamic, e.g., social networks. |
| Approach: | They propose to extract high-order neighborhood information at each given timestamp and then use an embedding prediction framework to capture the temporal correlations. |
| Outcome: | Extensive experiments on four real-world datasets show that the proposed method outperforms baseline methods for dynamic link prediction and node classification tasks. |
Learning distributed sentence vectors with bi-directional 3D convolutions (2020.coling-main)
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| Challenge: | Existing methods that render words or characters into images separately, but instead use text's visual features as input, we use 3-dimensional convolutions to learn distributed sentence representation. |
| Approach: | They propose to use text's visual features as input to learn distributed sentence representation using 3-dimensional sentence tensors and multiple 3-dimensional convolutions with different lengths are applied to the sentence . |
| Outcome: | The proposed model performs well on several downstream natural language processing tasks. |
Don’t Invite BERT to Drink a Bottle: Modeling the Interpretation of Metonymies Using BERT and Distributional Representations (2020.coling-main)
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| Challenge: | a recent study has shown that metonymy is a productive and systematic process . linguistic and psycholinguistic studies support the idea that metnomic interpretations are based on lexical ambiguity . |
| Approach: | They compare BERT to a generalized event knowledge model to capture the meaning shift associated with metonymy. |
| Outcome: | The proposed model is good at predicting the meaning of metonymic expressions, the authors say . they show that the model can capture the meaning shift associated with metonymy . |
Neural Unsupervised Domain Adaptation in NLP—A Survey (2020.coling-main)
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| Challenge: | Deep neural networks excel at learning from labeled data, but learning from unlabeled data remains a challenge. |
| Approach: | They review neural unsupervised domain adaptation techniques which do not require labeled target domain data. |
| Outcome: | The proposed techniques are more challenging yet widely applicable. |
Exploring the Value of Personalized Word Embeddings (2020.coling-main)
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| Challenge: | a subset of words belonging to specific psycholinguistic categories vary more in their representations across users . combining generic and personalized word embeddings yields the best performance . |
| Approach: | They propose personalized word embeddings and compare their performance to generic ones . they show that personalized word representations can be leveraged for improved performance . |
| Outcome: | The proposed model can be used for authorship attribution. |
Do Neural Language Models Overcome Reporting Bias? (2020.coling-main)
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| Challenge: | Recent studies show that pre-trained language models can overcome reporting bias by estimating the plausibility of rare but unspoken facts. |
| Approach: | They revisit the experiments conducted by Gordon and Van Durme (2013) . they find that pre-trained language models overestimate the very rare . |
| Outcome: | The proposed approach overestimates the rare at the expense of the rare, while minimizing reporting bias. |
Interpretable Multi-headed Attention for Abstractive Summarization at Controllable Lengths (2020.coling-main)
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| Challenge: | Abstractive summarization at controllable lengths is a challenging task in natural language processing . high variance in screen-sizes often require extensive human supervision to perform these modifications. |
| Approach: | They propose a supervised method to construct abstractive summaries of a text document at controllable lengths using an interpretable multi-headed attention mechanism. |
| Outcome: | The proposed method outperforms baselines on two low-resource datasets in English by 14.70%. |
Scale down Transformer by Grouping Features for a Lightweight Character-level Language Model (2020.coling-main)
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| Challenge: | Existing approaches to character-level language modeling have suffered from high learning complexity caused by inherently long character sequences. |
| Approach: | They propose a method that efficiently reduces the computational cost and parameter size of Transformer by splitting feature space into multiple groups, factorizing the calculation paths, and reducing computations for the group interaction. |
| Outcome: | The proposed model reduces the computational cost and parameter size of Transformer on two benchmark tasks, enwik8 and text8, and it performs well. |
Attention Word Embedding (2020.coling-main)
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| Challenge: | Word embedding models learn semantically rich vector representations of words . popular word embedders include word2vec, GloVe, and fastText . |
| Approach: | They propose an AWE-S model which integrates the attention mechanism into the CBOW model and incorporates subword information. |
| Outcome: | The proposed model outperforms the state-of-the-art model on word similarity datasets and when used for initialization of NLP models. |
CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters (2020.coling-main)
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Hicham El Boukkouri, Olivier Ferret, Thomas Lavergne, Hiroshi Noji, Pierre Zweigenbaum, Jun’ichi Tsujii
| Challenge: | Pre-trained language representations from Transformers have become the most popular choice for building NLP systems. |
| Approach: | They propose a new variant of BERT that drops the wordpiece tokenization system altogether . they propose 'characterBERT' module to represent entire words by consulting their characters . |
| Outcome: | The proposed model improves performance on a variety of medical domain tasks while producing robust, word-level, and open-vocabulary representations. |
Autoregressive Reasoning over Chains of Facts with Transformers (2020.coling-main)
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| Challenge: | Existing work on this task either evaluates facts in isolation or artificially limits the possible chains of facts, thus limiting multi-hop inference. |
| Approach: | They propose an iterative inference algorithm that decomposes the selection of facts from a corpus autoregressively and conditioning the next iteration on previously selected facts. |
| Outcome: | The proposed method outperforms the previous state-of-the-art in terms of precision, training time and inference efficiency on the WorldTree dataset. |
Augmenting NLP models using Latent Feature Interpolations (2020.coling-main)
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| Challenge: | Existing data augmentation methods with a large number of parameters are prone to over-fitting and often fail to capture the underlying input distribution. |
| Approach: | They propose a data augmentation technique that uses embeddings and hidden layer representations to construct virtual examples. |
| Outcome: | The proposed method outperforms existing methods in terms of accuracy and robustness to weight pruning. |
Neural Language Modeling for Named Entity Recognition (2020.coling-main)
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| Challenge: | Experimental results show that named entity recognition systems are faster and more flexible for the size of the corpus. |
| Approach: | They propose to use a neural language model as an alternative to the conditional random field layer for named entity recognition. |
| Outcome: | The proposed system has a significant speed advantage with a marginal performance degradation. |