Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues (P19-1)
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| Challenge: | Currently, retrieval-based dialogues are performed in shallow ways . a recent study investigated the problem of context-response matching in open-domain . |
| Approach: | They propose a model that lets utterance-response interaction go deep by stacking interaction blocks. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three benchmark data sets. |
Incremental Transformer with Deliberation Decoder for Document Grounded Conversations (P19-1)
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| Challenge: | Existing dialogue systems do not exploit document knowledge effectively enough. |
| Approach: | They propose a Transformer-based architecture for document grounded conversations that incorporates document knowledge into a two-pass decoder to improve context coherence and knowledge correctness. |
| Outcome: | The proposed model outperforms baselines on context coherence and knowledge relevance on a real-world document grounded dataset. |
Improving Multi-turn Dialogue Modelling with Utterance ReWriter (P19-1)
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| Challenge: | Recent research has achieved impressive results in single-turn dialogue modelling, but multi-turn models still remain challenging. |
| Approach: | They propose to rewrite human utterances as a pre-process to help multi-turn dialgoue modelling. |
| Outcome: | The proposed architecture achieves remarkably good performance on the utterance rewriting task. |
Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study (P19-1)
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| Challenge: | Neural generative models are becoming more popular when building conversational agents. |
| Approach: | They propose to study the sensitivity of neural dialog models to unnatural perturbations . they experiment with 10 different types of perturbations on 4 multi-turn dialog datasets . |
| Outcome: | The proposed model is sensitive to unnatural changes or perturbations on 4 multi-turn dialog datasets. |
Boosting Dialog Response Generation (P19-1)
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| Challenge: | Neural models generate the most common and generic responses all the time . Empirical results show that our method can significantly improve the diversity of responses generated by sequence-to-sequence models. |
| Approach: | They propose an iterative training process and ensemble method based on boosting to improve the diversity of responses generated by neural models. |
| Outcome: | Empirical results show that the proposed method significantly improves diversity and relevance of responses generated by all models. |
Constructing Interpretive Spatio-Temporal Features for Multi-Turn Responses Selection (P19-1)
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| Challenge: | Existing models for response selection do not perform well when there are many candidate responses. |
| Approach: | They propose a Spatio-Temporal Matching network (STM) for response selection . they use soft alignment to obtain local relevance between context and response . |
| Outcome: | The proposed model significantly outperforms the state-of-the-art model on two large-scale multi-turn response selection tasks. |
Semantic Parsing with Dual Learning (P19-1)
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| Challenge: | Existing approaches to parse natural language queries are limited by lack of labeled data and constrained decoding. |
| Approach: | They propose a semantic parsing framework with the dual learning algorithm that makes full use of data through a dual-learning game. |
| Outcome: | The proposed approach achieves state-of-the-art performance on ATIS dataset and gets competitive performance on overnight dataset. |
Semantic Expressive Capacity with Bounded Memory (P19-1)
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| Challenge: | Existing methods for semantic parsing use placeholders to represent relations between sentences and semantic representations. |
| Approach: | They show that compositional parsers can remember unbounded number of placeholders . this is the first study of this kind to describe relations between sentences and semantic representations based on projective mechanisms. |
| Outcome: | The proposed method can represent relations between sentences and semantic representations without using nonprojective mechanisms. |
AMR Parsing as Sequence-to-Graph Transduction (P19-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsing is the task of transducing natural language text into AMR, a graphbased formalism used for capturing sentence-level semantics. |
| Approach: | They propose a model that treats AMR parsing as sequence-to-graph transduction by aligner-free, and can be effectively trained with limited amounts of labeled AMR data. |
| Outcome: | The proposed model outperforms all previously reported SMATCH scores on AMR 2.0 (76.3%) and AMR 1.0 (70.2%). |
Generating Logical Forms from Graph Representations of Text and Entities (P19-1)
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| Challenge: | Recent approaches to semantic parsing have cast it as a sequence-to-sequence task, with strong results. |
| Approach: | They propose a Graph Neural Network architecture to incorporate information about relevant entities and their relations during parsing. |
| Outcome: | The proposed approach outperforms the state-of-the-art in several tasks without pre-training and outperformed existing approaches when combined with BERT pre-trainment. |
Learning Compressed Sentence Representations for On-Device Text Processing (P19-1)
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Dinghan Shen, Pengyu Cheng, Dhanasekar Sundararaman, Xinyuan Zhang, Qian Yang, Meng Tang, Asli Celikyilmaz, Lawrence Carin
| Challenge: | Existing methods for learning sentence embeddings assume they are continuous and real-valued. |
| Approach: | They propose four different strategies to transform continuous and generic sentence embeddings into a binarized form while preserving their rich semantic information. |
| Outcome: | The proposed methods reduce storage requirements by over 98% and improve performance on downstream tasks. |
The (Non-)Utility of Structural Features in BiLSTM-based Dependency Parsers (P19-1)
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| Challenge: | Existing non-neural dependency parsers benefit from information coming from structural features . however, their successors lack explicit information about the structural context . |
| Approach: | They propose to model the structural context of a biLSTM-based dependency parser with features built from partial subtrees. |
| Outcome: | The proposed models do not use any conventional structural features but capture implicitly the structural context. |
Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation (P19-1)
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| Challenge: | Existing methods for Combinatory Categorial Grammar (CCG) parsing are limited to a specific parser architecture, making it non-trivial to apply to current parsers. |
| Approach: | They propose a domain adaptation method for Combinatory Categorial Grammar (CCG) they propose to generate CCG corpora using cheaper dependency trees. |
| Outcome: | The proposed method improves on speech conversation and math problems. |
A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy (P19-1)
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| Challenge: | Named-entity recognition (NER) has seen significant progress with the application of Neural Networks to the task. |
| Approach: | They propose to use a given tag hierarchy to jointly learn a neural network that shares its tagging layer among all tag-sets. |
| Outcome: | The proposed model outperforms models that combine independent models and multitasking approaches in a domain adaptation for named-entity recognition task. |
Massively Multilingual Transfer for NER (P19-1)
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| Challenge: | Existing approaches for cross-lingual transfer use a single source language, but there are exceptions. |
| Approach: | They propose two techniques for modulating the transfer, suitable for zero-shot or few-shot learning, respectively. |
| Outcome: | The proposed methods are much more effective than baseline models and rival oracle selection of the single best individual model. |
Reliability-aware Dynamic Feature Composition for Name Tagging (P19-1)
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| Challenge: | Word embeddings are used to encode semantic information, but their quality is not consistent across the vocabulary due to the long-tail distribution of word frequency. |
| Approach: | They propose a reliability-aware name tagging model that uses word frequency to indicate word quality . they propose to use word frequency-based reliability signals to dynamically select and compose features . |
| Outcome: | The proposed model outperforms the baseline model on OntoNotes 5.0 and up to 5% gain on cross-genre data sets. |
Unsupervised Pivot Translation for Distant Languages (P19-1)
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| Challenge: | Unsupervised neural machine translation (NMT) is a popular method for transferring information between languages. |
| Approach: | They propose an unsupervised pivot translation method which translates a language to a distant language through multiple hops. |
| Outcome: | The proposed method improves translation on 20 languages and 294 distant languages on 20 different languages and language pairs. |
Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces (P19-1)
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| Challenge: | Recent work on bilingual lexicon induction (BLI) relies on an assumption about the isometry of two embedding spaces. |
| Approach: | They propose a semi-supervised approach that relaxes the isometric assumption while leveraging limited aligned bilingual lexicons and a larger set of unaligned word embeddings. |
| Outcome: | The proposed method obtains state-of-the-art results on 15 of 18 language pairs on the MUSE dataset and does particularly well when the embedding spaces don’t appear isometric. |
An Effective Approach to Unsupervised Machine Translation (P19-1)
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| Challenge: | a recent research line has managed to train both unsupervised and unsupervised machine translation systems using monolingual corpora only. |
| Approach: | They propose to use monolingual corpora to train both unsupervised and unsupervised machine translation systems. |
| Outcome: | The proposed system achieves 22.5 BLEU points in English-to-German WMT 2014, 5.5 points more than the previous best unsupervised system, and 0.5 points more in the (supervised) shared task winner back in 2014. |
Effective Adversarial Regularization for Neural Machine Translation (P19-1)
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| Challenge: | Existing (small) perturbations that induce a critical prediction error in machine learning models are often referred to as adversarial examples. |
| Approach: | They propose to use adversarial perturbations to regularize text classification tasks by adding adversarials to a typical NMT model structure. |
| Outcome: | The proposed method significantly improves performance of NMT models, such as LSTM-based and Transformer-based models. |
Revisiting Low-Resource Neural Machine Translation: A Case Study (P19-1)
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| Challenge: | Recent research has shown that neural machine translation models are highly data-inefficient and underperform phrase-based statistical machine translation (PBSMT) in low-resource settings. |
| Approach: | They propose to use auxiliary data to train low-resource neural machine translation systems without auxiliary monolingual or multilingual data. |
| Outcome: | The proposed methods outperform PBSMT and other statistical machine translation models in Korean–English with minimal data. |
Domain Adaptive Inference for Neural Machine Translation (P19-1)
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| Challenge: | Neural Machine Translation models are effective when trained on broad domains with large datasets, such as news translation. |
| Approach: | They propose a novel approach for adaptive ensemble weighting for Neural Machine Translation by extending Bayesian Interpolation with source information. |
| Outcome: | The proposed approach improves performance on Spanish-English and English-German tasks without the need for the domain label. |
Neural Relation Extraction for Knowledge Base Enrichment (P19-1)
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| Challenge: | Existing studies focus on the extraction itself and rely on Named Entity Disambiguation (NED) to map triples into knowledge base (KB) enrichment. |
| Approach: | They propose an end-to-end relation extraction model for knowledge base enrichment based on a neural encoder-decoder model . they propose to extract entities and their relationships from sentences in the form of triples and map the elements of the extracted triples to an existing KB in an end to end manner. |
| Outcome: | The proposed model outperforms state-of-the-art baselines by 15.51% and 8.38% on two real-world datasets. |
Attention Guided Graph Convolutional Networks for Relation Extraction (P19-1)
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| Challenge: | Existing approaches for selecting relevant partial dependency structures may not yield optimal results. |
| Approach: | They propose a model which directly takes full dependency trees as inputs and uses them to selectively attend to relevant sub-structures. |
| Outcome: | The proposed model can be understood as a soft-pruning approach that automatically learns how to selectively attend to the relevant sub-structures useful for the relation extraction task. |
Spatial Aggregation Facilitates Discovery of Spatial Topics (P19-1)
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| Challenge: | a recent paper shows that topic discovery is a powerful tool for describing pertinent themes in a document corpus. |
| Approach: | They propose to use a traditional topic discovery algorithm to find spatially distinct topics by spatial aggregation. |
| Outcome: | The proposed method can efficiently discover spatially distinct topics from large documents. |
Relation Embedding with Dihedral Group in Knowledge Graph (P19-1)
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| Challenge: | Existing models for link prediction fail to capture relation compositions, resulting in lack of interpretability for reasoning on incomplete knowledge graph (KG). |
| Approach: | They propose a new model that learns knowledge graph embeddings that can capture relation compositions by nature and reduces the solution space drastically. |
| Outcome: | The proposed model outperforms existing models and is comparable to or better than deep learning models such as ConvE. |
Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation (P19-1)
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| Challenge: | Pretrained contextual and non-contextual subword embeddings are available in over 250 languages, allowing massively multilingual NLP. |
| Approach: | They compare pretrained contextual and non-contextual subword embeddings with a contextual representation method, namely BERT, on multilingual named entity recognition and part-of-speech tagging. |
| Outcome: | The proposed method outperforms non-contextual embeddings on multilingual named entity recognition and part-of-speech tagging. |
Augmenting Neural Networks with First-order Logic (P19-1)
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| Challenge: | Existing paradigms for training neural networks require large datasets, a paper argues . we present a framework for introducing declarative knowledge to neural networks . |
| Approach: | They propose a framework for introducing declarative knowledge to neural networks . they compile logical statements into graphs that augment a network without extra learnable parameters or manual redesign. |
| Outcome: | The proposed framework improves on three tasks, especially in low-data regimes. |
Self-Regulated Interactive Sequence-to-Sequence Learning (P19-1)
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| Challenge: | Existing approaches to learning from different types of feedback have not been explored. |
| Approach: | They propose a machine learning algorithm that uses self-regulation to balance cost and effect of different types of feedback. |
| Outcome: | The proposed model is robust under domain shift and is a promising alternative to active learning. |
You Only Need Attention to Traverse Trees (P19-1)
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| Challenge: | Recent research has focused on sentence representations. |
| Approach: | They propose a tree-based model that captures phrase-level syntax and word-level dependencies by doing recursive traversal with attention. |
| Outcome: | a new model captures phrase-level syntax and word-level dependencies with attention. |
Cross-Domain Generalization of Neural Constituency Parsers (P19-1)
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| Challenge: | Neural parsers perform well on in-domain benchmarks, but their performance degrades in well-understood ways. |
| Approach: | They analyze generalization on English and Chinese corpora to see if they can generalize to other domains. |
| Outcome: | The proposed neural parsers perform better on in-domain benchmarks than on out-of-domain corpora. |
Adaptive Attention Span in Transformers (P19-1)
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| Challenge: | We extend the maximum context size of a neural network called Transformer to 8k characters. |
| Approach: | They propose a self-attention mechanism that can learn its optimal attention span . this allows for models with longer context and the capability to catch longer dependencies. |
| Outcome: | The proposed model achieves state-of-the-art performance on text8 and enwiki8 using 8k characters with no loss of performance, and maintains control over memory footprint and computational time. |
Neural News Recommendation with Long- and Short-term User Representations (P19-1)
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| Challenge: | Existing news recommendation methods learn a single representation for each user, which may be insufficient. |
| Approach: | They propose a neural news recommendation approach which can learn both long- and short-term user representations by using a news encoder and a user encoder. |
| Outcome: | The proposed approach can learn both long- and short-term user representations on a real-world dataset. |
Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes (P19-1)
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| Challenge: | In this paper, we automatically create sentiment dictionaries for predicting financial outcomes excess return and volatility. |
| Approach: | They propose to automatically adapt a domain-general dictionary to a financial domain and then manually adapt it to dictionaries for the finance domain. |
| Outcome: | The proposed dictionary outperforms the previous state of the art in predicting financial variables excess return and volatility. |
Manipulating the Difficulty of C-Tests (P19-1)
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| Challenge: | a new manipulation strategy for increasing and decreasing difficulty of C-tests is proposed for language learning . we use two strategies to generate C- tests with the desired difficulty level . learning languages is of utmost importance in an international society . |
| Approach: | They propose two manipulation strategies for increasing and decreasing difficulty of C-tests automatically. |
| Outcome: | The proposed manipulation strategies increase and decrease difficulty of C-tests automatically. |
Towards Unsupervised Text Classification Leveraging Experts and Word Embeddings (P19-1)
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| Challenge: | a new method for text classification uses supervised and semisupervised approaches to classify documents into categories. |
| Approach: | They propose an unsupervised method to classify documents into categories simply described by a label. |
| Outcome: | The proposed method increases F1-score over relying on human expertise and language models on standard corpora. |
Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table (P19-1)
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| Challenge: | Clinical letters are written by doctors and typically contain complex medical language that is beyond the scope of the lay reader. |
| Approach: | They propose to augment existing neural text simplification software with a phrase table that links medical terminology to simpler vocabulary by mining SNOMED-CT data. |
| Outcome: | The proposed system is easier to understand than the existing system and the phrase table without it. |
What You Say and How You Say It Matters: Predicting Stock Volatility Using Verbal and Vocal Cues (P19-1)
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| Challenge: | Existing studies have shown that textual information in a firm’s financial statement can be used to predict its stock’s risk level. |
| Approach: | They propose to model CEO’s verbal (from text) and vocal (from audio) information in a conference call. |
| Outcome: | The proposed model reduces the error rate by comparing CEO’s verbal and vocal information in a conference call with other models. |
Detecting Concealed Information in Text and Speech (P19-1)
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| Challenge: | despite the importance and potential impact of detecting concealed information, research on detecting it has been scarce. |
| Approach: | They propose a multi-task learning framework that automatically detects concealed information from text and speech using acoustic-prosodic, linguistic, and individual feature sets. |
| Outcome: | The proposed framework outperforms human performance by 15% in acoustic, linguistic, and individual features. |
Evidence-based Trustworthiness (P19-1)
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| Challenge: | Existing methods to estimate the trustworthiness of information sources are local in that they apply to a given claim. |
| Approach: | They propose a framework for estimating the trustworthiness of information sources in an environment where multiple sources provide claims and supporting evidence. |
| Outcome: | The proposed models show that they improve on baselines and show that the proposed models are more accurate than baselines. |
Disentangled Representation Learning for Non-Parallel Text Style Transfer (P19-1)
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| Challenge: | a paper aims to disentangle latent representations of style and content in language models . auxiliary multi-task and adversarial objectives are used to disentangle the latent space . |
| Approach: | They propose a simple yet effective approach to disentangling latent representations . they propose auxiliary multi-task and adversarial objectives to disentangle style and content . |
| Outcome: | The proposed approach achieves high performance in terms of transfer accuracy, content preservation, and language fluency compared to previous approaches . |
Cross-Sentence Grammatical Error Correction (P19-1)
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| Challenge: | Existing approaches to automatic grammatical error correction (GEC) ignore cross-sentence context . existing approaches only correct one sentence at a time and ignore useful contextual information . |
| Approach: | They propose to use an auxiliary encoder that encodes previous sentences and incorporates the encoding in the decoder via attention and gating mechanisms. |
| Outcome: | The proposed model improves over strong baselines on a synthetic dataset showing high performance in verb tense corrections that require cross-sentence context. |
This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation (P19-1)
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| Challenge: | Existing research tracks on email use focus on email summarization, email keyword extraction and action detection. |
| Approach: | They propose to use email body to automatically generate an email subject line from the email body. |
| Outcome: | The proposed method outperforms baselines and state-of-the-art systems in the evaluation of human and automatic metrics. |
Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change (P19-1)
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| Challenge: | State-of-the-art lexical semantic change detection models suffer from noise stemming from vector space alignment. |
| Approach: | They propose a method to simulate lexical semantic change and control for possible biases by avoiding alignment. |
| Outcome: | The proposed method outperforms state-of-the-art models on a synthetic task and a manual testset. |
Adversarial Attention Modeling for Multi-dimensional Emotion Regression (P19-1)
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| Challenge: | Empirical evaluation on EMOBANK corpus shows that our approach achieves notable improvements in r-values over the state-of-the-art baselines. |
| Approach: | They propose a neural network-based approach to multi-dimensional emotion regression which automatically rates multiple emotion dimension scores for an input text. |
| Outcome: | The proposed approach achieves notable improvements in r-values on both EMOBANK Reader’s and Writer’s multi-dimensional emotion regression tasks over the state-of-the-art baselines. |
Divide, Conquer and Combine: Hierarchical Feature Fusion Network with Local and Global Perspectives for Multimodal Affective Computing (P19-1)
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| Challenge: | Existing approaches to multimodal fusion are based on fusing features at holistic level instead of focusing on local and global interactions. |
| Approach: | They propose a general strategy called ‘divide, conquer and combine’ for multimodal fusion that combines local and global interactions in a hierarchy. |
| Outcome: | The proposed strategy achieves state-of-the-art performance on multimodal affective computing with higher efficiency. |
Modeling Financial Analysts’ Decision Making via the Pragmatics and Semantics of Earnings Calls (P19-1)
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| Challenge: | Existing studies show earnings calls influence investor sentiment and influence investor opinions in the short term. |
| Approach: | They identify 20 pragmatic features of analysts’ questions which correlate with analysts’ pre-call investor recommendations. |
| Outcome: | The proposed model shows that earnings calls are moderately predictive of analysts’ decisions even though these decisions are influenced by market conditions and private communications. |
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis (P19-1)
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| Challenge: | Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a sentence. |
| Approach: | They propose an interactive multi-task learning network which can learn multiple tasks simultaneously . they use a shared set of latent variables to iteratively pass information between tasks . |
| Outcome: | The proposed method outperforms existing methods on three benchmark datasets. |
Decompositional Argument Mining: A General Purpose Approach for Argument Graph Construction (P19-1)
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| Challenge: | Argument mining is the process of identifying argumentative structure contained within a text. |
| Approach: | They propose to decompose propositions into four functional components and identify the patterns linking those components to determine argument structure. |
| Outcome: | The proposed method is generic in that it is not tuned for a specific corpus and achieved an F score of 0.79, 0.77 and 0.64 respectively. |
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations (P19-1)
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| Challenge: | Emotion recognition in conversations has gained popularity due to its potential applications. Until now, a large multimodal multi-party emotional conversational database containing more than two speakers per dialogue was missing. |
| Approach: | They propose to extend and enhance EmotionLines by combining 13,000 utterances from Friends dialogues with emotion and sentiment labels. |
| Outcome: | The proposed dataset contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends. |
Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification (P19-1)
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| Challenge: | Existing approaches to target sentiment analysis are limited by huge search space and sentiment inconsistency. |
| Approach: | They propose a span-based extract-then-classify framework to detect opinion targets . they propose pipeline, joint, and collapsed models to classify polarities . |
| Outcome: | The proposed framework outperforms the sequence tagging baseline on three benchmark datasets. |
Transfer Capsule Network for Aspect Level Sentiment Classification (P19-1)
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| Challenge: | Lack of aspect-level labeled data is a major obstacle in sentiment classification due to high cost . document-level labels like reviews are easily accessible from online websites . |
| Approach: | They propose a transfer capsule network model for transferring document-level knowledge to aspect-level sentiment classification by encapsulating sentence-level semantic representations into semantic capsules. |
| Outcome: | The proposed model can transfer document-level knowledge to aspect-level sentiment classification. |
Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis (P19-1)
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| Challenge: | Experimental results show that our proposed approach yields better attention mechanisms . dominant ASC models are mostly discriminative classifiers based on manual feature engineering . |
| Approach: | They propose a self-supervised approach to aspect-level sentiment classification that mines useful attention supervision information from a training corpus to refine attention mechanisms. |
| Outcome: | The proposed approach yields better attention mechanisms on multiple datasets. |
Classification and Clustering of Arguments with Contextualized Word Embeddings (P19-1)
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| Challenge: | Existing methods for argument mining focus on analyzing local argumentation structures, but information-seeking approaches need to be able to deal with heterogeneous sources and topics. |
| Approach: | They propose to use contextualized word embeddings to classify and cluster topic-dependent arguments using a UKP Sentential Argument Mining Corpus and IBM Debater - Evidence Sentences datasets. |
| Outcome: | The proposed method improves state-of-the-art on argument classification and clustering tasks and across multiple datasets. |
Sentiment Tagging with Partial Labels using Modular Architectures (P19-1)
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| Challenge: | Many NLP learning tasks can be decomposed into sub-tasks, each associated with a partial label. |
| Approach: | They propose a modular learning approach where sub-tasks are learned using functional modules . they propose 'learning with partial labels' approach that decomposes tasks into partial labels . |
| Outcome: | The proposed approach can simplify learning and reduce supervision efforts. |
DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction (P19-1)
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| Challenge: | Existing algorithms address aspect term extraction and aspect sentiment classification as separate tasks, which can be complicated for real applications. |
| Approach: | They propose a dual crOss-sharEd RNN framework to generate all aspect term-polarity pairs of the input sentence simultaneously. |
| Outcome: | The proposed framework outperforms state-of-the-art frameworks on three benchmark datasets. |
A Corpus for Modeling User and Language Effects in Argumentation on Online Debating (P19-1)
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| Challenge: | Existing argumentation datasets have allowed only limited assessment of "user" traits because information on background of users is generally unavailable. |
| Approach: | They present a dataset of 78,376 debates generated over a 10-year period along with surprisingly comprehensive participant profiles. |
| Outcome: | The proposed dataset includes 78,376 debates generated over a 10-year period along with comprehensive participant profiles. |
Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese (P19-1)
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| Challenge: | Currently, most studies on implicit discourse relation recognition use sentence-level representations . Chinese is a paratactic language that tends to pro-drop clause connectives . |
| Approach: | They propose a topic tensor network to recognize Chinese implicit discourse relations with both sentence-level and topic-level representations. |
| Outcome: | The proposed model outperforms state-of-the-art models in micro and macro F1 scores on a Chinese discourse corpus. |
Learning from Omission (P19-1)
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| Challenge: | a recent study shows that pragmatic reasoning improves language understanding systems . end-to-end training produces more accurate utterance interpretation models . |
| Approach: | They show that pragmatic reasoning can improve the quality of learned meanings . they draw pragmatic inferences from listening to what a speaker says and not to what they do . |
| Outcome: | The proposed model improves the quality of language understanding models when sparse data is used. |
Multi-Task Learning for Coherence Modeling (P19-1)
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| Challenge: | Existing models for assessing discourse coherence have been developed for summarization and language assessment. |
| Approach: | They propose a hierarchical neural network that learns to predict a document-level coherence score along with word-level grammatical roles, taking advantage of inductive transfer between the two tasks. |
| Outcome: | The proposed framework can predict document-level coherence score and word-level grammatical roles using inductive transfer between the two tasks. |
Data Programming for Learning Discourse Structure (P19-1)
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| Challenge: | Discourse structures are a relational semantic structure that convey causal, topical, argumentative relations or more generally coherence relations. |
| Approach: | They propose to use Snorkel to label training data using expert-composed heuristics and transform them into probability distributions of the class labels given to training candidates. |
| Outcome: | The proposed paradigm can be used for difficult tasks such as that of discourse attachment. |
Evaluating Discourse in Structured Text Representations (P19-1)
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| Challenge: | Discourse structure is integral to understanding a text and is useful in many NLP tasks. |
| Approach: | They propose a structured attention mechanism for text classification that derives a tree over a text, akin to an RST discourse tree. |
| Outcome: | The proposed model improves performance on multiple discourse-relevant tasks and datasets and ablation studies show it does little to capture discourse structure. |
Know What You Don’t Know: Modeling a Pragmatic Speaker that Refers to Objects of Unknown Categories (P19-1)
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| Challenge: | a lot of recent and traditional research on pragmatically informative object descriptions has focused on the task of correctly labelling objects of novel categories. |
| Approach: | They extend a neural generator to become a pragmatic speaker reasoning about uncertain object categories. |
| Outcome: | The proposed model improves the accuracy of the listener's communication with unfamiliar objects. |
End-to-end Deep Reinforcement Learning Based Coreference Resolution (P19-1)
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| Challenge: | Recent neural network models for coreference resolution are usually trained with heuristic loss functions that are computed over a sequence of local decisions. |
| Approach: | They propose an end-to-end reinforcement learning based coreference resolution model to directly optimize coreference evaluation metrics. |
| Outcome: | The proposed model achieves new state-of-the-art performance on the English OntoNotes v5.0 benchmark. |
Implicit Discourse Relation Identification for Open-domain Dialogues (P19-1)
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| Challenge: | Discourse relation identification is a challenging problem in open-domain dialogue systems . previous work relies on formal text but this data is not suitable for informal dialogue . |
| Approach: | They propose a method to automatically extract the implicit discourse relation argument pairs from dialogic turns and a pipeline to identify them. |
| Outcome: | The proposed pipeline extracts argument pairs from dialogic turns and improves it by performing feature ablation and incorporating dialogue features. |
Coreference Resolution with Entity Equalization (P19-1)
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| Challenge: | Existing approaches to coreference resolution capture the properties of entity clusters and use them in the resolution process. |
| Approach: | They propose an approach that captures entities and uses them in coreference resolution . they propose an "Entity Equalization" mechanism that represents each mention in a cluster . |
| Outcome: | The proposed approach improves the CoNLL-2012 coreference resolution task by 3.6%. |
A Cross-Domain Transferable Neural Coherence Model (P19-1)
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Peng Xu, Hamidreza Saghir, Jin Sung Kang, Teng Long, Avishek Joey Bose, Yanshuai Cao, Jackie Chi Kit Cheung
| Challenge: | Existing coherence models do not generalize to unseen categories of text . previous work advocates for generative models for cross-domain generalization . |
| Approach: | They propose a local discriminative neural model with a smaller negative sampling space that can discriminate against incorrect orderings. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a standard benchmark dataset on the Wall Street Journal corpus and multiple challenging settings on Wikipedia articles. |
MOROCO: The Moldavian and Romanian Dialectal Corpus (P19-1)
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| Challenge: | Using the MOldavian and ROmanian Dialectal COrpus, we perform empirical studies on dialect identification tasks. |
| Approach: | They introduce the MOldavian and ROmanian Dialectal COrpus corpus which contains 33564 samples of text collected from the news domain. |
| Outcome: | The proposed model is based on a shallow and deep approach to discriminate between two different languages. |
Just “OneSeC” for Producing Multilingual Sense-Annotated Data (P19-1)
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| Challenge: | Word Sense Disambiguation (WSD) is one of the most affected research areas . annotated data are scarce in English and almost absent in other languages . |
| Approach: | They propose a language-independent method for the automatic extraction of thousands of sentences in which a target word is tagged with its meaning. |
| Outcome: | The proposed method outperforms existing methods on multilingual and domain-specific settings. |
How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions (P19-1)
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| Challenge: | Cross-lingual word embeddings (CLEs) are used for downstream NLP tasks . CLEs are based on bilingual lexicon induction (BLI) evaluations vary greatly, hindering ability to interpret performance and properties of different CLE models. |
| Approach: | They evaluate CLE models for a large number of language pairs on bilingual lexicon induction and three downstream tasks. |
| Outcome: | The proposed model performance is based on supervised and unsupervised models on bilingual lexicon induction and three downstream tasks. |
SP-10K: A Large-scale Evaluation Set for Selectional Preference Acquisition (P19-1)
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| Challenge: | Selectional Preference (SP) is a common phenomenon in human language and has been shown to be useful in many natural language processing tasks. |
| Approach: | They propose a large-scale evaluation set that provides human ratings for the plausibility of 10,000 SP pairs over five SP relations, covering 2,500 most frequent verbs, nouns, and adjectives in American English. |
| Outcome: | The proposed evaluation sets provide human ratings for plausibility of 10,000 SP pairs over five SP relations covering 2,500 most frequent verbs, nouns, and adjectives in American English. |
A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains (P19-1)
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| Challenge: | Existing models for diachronic and synchronic detection of lexical semantic divergences are superficial and lack of comparison. |
| Approach: | They propose to extend benchmark models on a common state-of-the-art evaluation task . they also demonstrate that the same evaluation task and modelling approaches can be utilised for synchronic detection of domain-specific sense divergences in the field of term extraction. |
| Outcome: | The proposed model can be utilised for the detection of domain-specific sense divergences in the field of term extraction. |
Errudite: Scalable, Reproducible, and Testable Error Analysis (P19-1)
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| Challenge: | Errudite is an interactive tool for informative error analysis. |
| Approach: | They propose an interactive tool for model and task agnostic error analysis . they propose a model-agnosed approach to model and tasks error analysis with Errudite . |
| Outcome: | The proposed tool enables reproducible error analyses with less effort and ambiguities in previous tools. |
DocRED: A Large-Scale Document-Level Relation Extraction Dataset (P19-1)
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Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun
| Challenge: | Existing relation extraction methods focus on extracting intra-sentence relations for single entities. |
| Approach: | They propose a relation extraction dataset from Wikipedia and Wikidata with three features . document-level relation extraction is a task to identify relational facts between entities . |
| Outcome: | The proposed dataset is the largest human-annotated dataset for document-level RE from plain text. |
ChID: A Large-scale Chinese IDiom Dataset for Cloze Test (P19-1)
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| Challenge: | cloze-style reading comprehension in Chinese is limited due to the lack of various corpora. |
| Approach: | They propose a large-scale Chinese cloze test dataset ChID which studies the comprehension of idiom in Chinese. |
| Outcome: | The proposed dataset compares the performance of the proposed model with human models. |
Automatic Evaluation of Local Topic Quality (P19-1)
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Jeffrey Lund, Piper Armstrong, Wilson Fearn, Stephen Cowley, Courtni Byun, Jordan Boyd-Graber, Kevin Seppi
| Challenge: | Topic models are evaluated with global topic distributions but without local topic assignments. |
| Approach: | They propose a task to elicit human judgments of token-level topic assignments . they propose to use global metrics to evaluate topic models at a local level . |
| Outcome: | The proposed task elicits human judgments of token-level topic assignments . global metrics agree poorly with human assignments, the authors show . |
Crowdsourcing and Aggregating Nested Markable Annotations (P19-1)
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| Challenge: | Existing methods for identifying markables for coreference annotation are task and language-independent and can be used for a variety of other annotation tasks. |
| Approach: | They propose a method for identifying markables for coreference annotation that combines automatic markable detectors with checking with a Game-With-A-Purpose (GWAP) and aggregation using a Bayesian annotation model. |
| Outcome: | The proposed method improves mention boundaries on news and other genres by over seven percentage points compared with state-of-the-art, domain-independent automatic mention detectors and almost three points over an in-domain mention detector. |
Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems (P19-1)
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| Challenge: | Existing approaches to dialogue state tracking are dependent on domain ontology and lack of sharing knowledge across domains. |
| Approach: | They propose a transferable dialogue state generator that generates dialogue states from utterances using copy mechanism. |
| Outcome: | Empirical results show that TRADE achieves state-of-the-art 48.62% joint goal accuracy for the five domains of MultiWOZ. |
Multi-Task Networks with Universe, Group, and Task Feature Learning (P19-1)
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| Challenge: | In multi-task learning, multiple related tasks are learned together. |
| Approach: | They propose methods that take advantage of natural groupings of related tasks . they propose parallel and serial architectures that can learn different feature spaces . |
| Outcome: | The proposed methods improve performance on natural language understanding (NLU) tasks. |
Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue (P19-1)
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| Challenge: | Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems. |
| Approach: | They propose using tree-structured semantic representations for better discourse-level structuring and sentence-level planning and introduce a challenging dataset using this representation for the weather domain. |
| Outcome: | The proposed model improves discourse-level structuring and sentence-level planning on a weather domain and can be decoded to improve semantic correctness. |
OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs (P19-1)
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| Challenge: | Existing models that use a large-scale knowledge graph to create a conversational reasoning model are domain-agnostic and scalable. |
| Approach: | They propose a conversational reasoning model that strategically traverses through a large-scale common fact knowledge graph to introduce engaging and contextually diverse entities and attributes. |
| Outcome: | The proposed model retrieves more natural responses than state-of-the-art models in both in-domain and cross-domain tasks. |
Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing (P19-1)
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| Challenge: | a context-aware retrieval model and a meta-learning paradigm are used for context-dependent semantic parsing . |
| Approach: | They propose a retrieval model and a meta-learner to incorporate retrieved datapoints as context-dependent semantic parsing evidence. |
| Outcome: | The proposed approach performs better than retrieve-and-edit baselines on CONCODE and CSQA datasets. |
Knowledge-aware Pronoun Coreference Resolution (P19-1)
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| Challenge: | Existing models for pronoun coreference resolution only use triplets, the most common format for knowledge graphs. |
| Approach: | They propose a model that leverages different types of knowledge to resolve pronoun coreference with a neural model. |
| Outcome: | The proposed model outperforms state-of-the-art baselines on two datasets from different domains. |
Don’t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference (P19-1)
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| Challenge: | Natural Language Inference (NLI) datasets often contain hypothesis-only biases . authors propose probabilistic methods to build models that are more robust to such artifacts - a new study shows . |
| Approach: | They propose probabilistic methods to build models that are more robust to biases . authors train on datasets containing biase . |
| Outcome: | The proposed methods can make NLI models more robust to dataset-specific artifacts . the methods transfer better than a baseline architecture in 9 out of 12 NLI datasets compared with baseline architectures based on the proposed methods . |
GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification (P19-1)
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| Challenge: | Existing methods to extract information from evidence are unable to grasp relational and logical information among the evidence. |
| Approach: | They propose a graph-based evidence aggregating and reasoning framework to integrate evidence from multiple pieces of evidence. |
| Outcome: | The proposed framework achieves significant performance improvements on a large-scale benchmark dataset. |
SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference (P19-1)
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| Challenge: | We evaluate a large number of strong baselines on SherLIiC, ranging from semantic vector space models to state of the art neural models of natural language inference (NLI). |
| Approach: | They propose a testbed for lexical inference in context consisting of 3985 manually annotated inference rule candidates and a set of 960k unlabeled InfCands. |
| Outcome: | The proposed testbed is based on 3985 manually annotated inference rule candidates (InfCands) and 190k typed textual relations between Freebase entities extracted from the large entity-linked corpus ClueWeb09. |
Extracting Symptoms and their Status from Clinical Conversations (P19-1)
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| Challenge: | Existing models for extracting symptoms from clinical conversations are inherently difficult. |
| Approach: | They propose two new deep learning models tailored for a new application . they propose a hierarchical span-attribute tagging model and a sequence-to-sequence model . |
| Outcome: | The proposed models perform well under different conditions and are compared to existing models. |
What Makes a Good Counselor? Learning to Distinguish between High-quality and Low-quality Counseling Conversations (P19-1)
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| Challenge: | Qualitative counseling relies on active collaboration between clients and counselors . |
| Approach: | They propose to use linguistic features to capture differences between high- and low-quality counseling conversations to build automatic classifiers that can predict counseling quality with accuracies of up to 88%. |
| Outcome: | The proposed model can predict counseling quality with accuracies of up to 88%. |
Finding Your Voice: The Linguistic Development of Mental Health Counselors (P19-1)
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| Challenge: | a longitudinal study of mental health counseling shows that counselors change their conversational behavior to become more diverse across interactions. |
| Approach: | They propose a computational framework to quantify the extent to which individuals change their linguistic behavior with experience. |
| Outcome: | The proposed framework quantifies the extent to which individuals change their linguistic behavior with experience and examines the nature of this evolution. |
Towards Automating Healthcare Question Answering in a Noisy Multilingual Low-Resource Setting (P19-1)
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| Challenge: | a study aims to automate a multilingual digital helpdesk service available via text messaging to pregnant and breastfeeding mothers in South Africa. |
| Approach: | They examine a multilingual digital helpdesk service available via text messaging to pregnant and breastfeeding mothers in South Africa. |
| Outcome: | The proposed model can accelerate response time by several orders of magnitude. |
Joint Entity Extraction and Assertion Detection for Clinical Text (P19-1)
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| Challenge: | Existing systems for in-formation extraction treat negative medical findings as a pipeline of two separate tasks. |
| Approach: | They propose a multi-task neural model to jointly extract entities and negations from medical reports. |
| Outcome: | The proposed model performs considerably better than existing systems on a 2010 i2b2/VA challenge dataset and a proprietary de-identified clinical dataset. |
HEAD-QA: A Healthcare Dataset for Complex Reasoning (P19-1)
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| Challenge: | Recent progress in question answering has been led by neural models, but current methods are too data intensive and weak. |
| Approach: | They propose a multi-choice question answering testbed to encourage research on complex reasoning. |
| Outcome: | The proposed dataset is useful as a benchmark for future work. |
Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network (P19-1)
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Martin Gleize, Eyal Shnarch, Leshem Choshen, Lena Dankin, Guy Moshkowich, Ranit Aharonov, Noam Slonim
| Challenge: | Recent advances in argument detection have made it easier to identify the more convincing arguments. |
| Approach: | They propose a new data set of pairs of evidence labeled for convincingness that is more challenging than existing alternatives. |
| Outcome: | The proposed method outperforms baselines on convincingness data and its own. |
From Surrogacy to Adoption; From Bitcoin to Cryptocurrency: Debate Topic Expansion (P19-1)
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Roy Bar-Haim, Dalia Krieger, Orith Toledo-Ronen, Lilach Edelstein, Yonatan Bilu, Alon Halfon, Yoav Katz, Amir Menczel, Ranit Aharonov, Noam Slonim
| Challenge: | Recent advances in argumentation mining have left much of the relevant argumentative content out of reach. |
| Approach: | They propose a task of Debate Topic Expansion to find related topics for a given debate topic, along with an annotated dataset for the task. |
| Outcome: | The proposed algorithms differ from well-studied lexical-semantic relations and show they work well in argumentation mining. |
Multimodal and Multi-view Models for Emotion Recognition (P19-1)
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| Challenge: | combining lexical and acoustic information results in more robust and accurate models . combining both modalities may be a bottleneck in a deployment pipeline due to computational complexity or privacy constraints . |
| Approach: | They propose to combine acoustic and lexical information to provide a deployable acustic model . they use multimodal models and two attention mechanisms to assess the benefits of lexicals . |
| Outcome: | The proposed model outperforms the state-of-the-art on the USC-IEMOCAP dataset . it significantly surpasses models that have been exclusively trained with acoustic features . |
Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts (P19-1)
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| Challenge: | Emotion cause extraction (ECE) aims at extracting potential causes behind certain emotions in text. |
| Approach: | They propose a 2-step task to extract potential pairs of emotions and corresponding causes in a document. |
| Outcome: | The proposed task is based on a benchmark emotion cause corpus. |
Argument Invention from First Principles (P19-1)
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Yonatan Bilu, Ariel Gera, Daniel Hershcovich, Benjamin Sznajder, Dan Lahav, Guy Moshkowich, Anael Malet, Assaf Gavron, Noam Slonim
| Challenge: | Argument Invention is a task that is often referred to as a natural way of inventing arguments, but has not been formalized in the context of NLP. |
| Approach: | They propose to define a taxonomy of recurring arguments and to automatically identify which of them are relevant to the topic. |
| Outcome: | The proposed taxonomy is coherent, covers the relevant topics and coincides with what debaters actually argue in their speeches, and facilitates automatic argument invention for new topics. |
Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization (P19-1)
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| Challenge: | Despite the empirical success of multi-document summarization, most datasets remain small and the cost of hiring hu-1 is prohibitive. |
| Approach: | They propose a novel method for extractive multi-document summarization that measures redundancy between a pair of sentences based on surface form and semantic information. |
| Outcome: | The proposed method outperforms baseline methods on benchmark datasets and is particularly useful for documents created by multiple authors containing redundant yet lexically diverse expressions. |
Global Optimization under Length Constraint for Neural Text Summarization (P19-1)
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| Challenge: | GOLC increases the probabilities of generating summaries that have high evaluation scores within a desired length. |
| Approach: | They propose a global optimization method under length constraint for neural text summarization models. |
| Outcome: | The proposed method generates fewer overlength summaries while maintaining the fastest processing speed. |
Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)
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| Challenge: | Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved. |
| Approach: | They propose to use different types of model architectures to improve extractive summarization systems. |
| Outcome: | The proposed framework achieves state-of-the-art on CNN/DailyMail by a large margin based on observations and analysis. |
A Simple Theoretical Model of Importance for Summarization (P19-1)
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| Challenge: | Existing empirical summarization frameworks only identify signals correlating with the vague human intuition of Importance. |
| Approach: | They propose to define several concepts that were previously used only intuitively in summarization: redundancy, Relevance, and Informativeness. |
| Outcome: | The proposed models will improve summarization systems and improve their performance on standard datasets, while the notion of Importance remains latent. |
Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model (P19-1)
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| Challenge: | Multi-document summarization (MDS) of news articles has been limited to datasets of a couple of hundred examples. |
| Approach: | They propose a model which integrates a traditional extractive summarization model with a standard SDS model and achieves competitive results on MDS datasets. |
| Outcome: | The proposed model achieves competitive results on large-scale datasets. |
Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency (P19-1)
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| Challenge: | Existing approaches to attack text classification are limited due to the complexity of the problem. |
| Approach: | They propose a greedy algorithm to generate adversarial examples that maintain lexical correctness, grammatical correctiness and semantic similarity. |
| Outcome: | The proposed algorithm maintains lexical correctness, grammatical correctity and semantic similarity well and is hard for humans to perceive. |
Heuristic Authorship Obfuscation (P19-1)
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| Challenge: | Existing methods for authorship verification are insufficient to control the authorial style of a text. |
| Approach: | They propose a novel method that models writing style difference as the Jensen-Shannon distance between character n-gram distributions of texts and manipulates an author’s subconsciously encoded writing style using heuristic search. |
| Outcome: | The proposed approach defeats state-of-the-art verification approaches while keeping text changes at a minimum. |
Text Categorization by Learning Predominant Sense of Words as Auxiliary Task (P19-1)
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| Challenge: | Existing methods for text categorization use implicit representations to learn the senses of words. |
| Approach: | They propose a method for text categorization by leveraging the predominant sense of words depending on the domain. |
| Outcome: | The proposed model improves performance on four benchmark datasets. |
DeepSentiPeer: Harnessing Sentiment in Review Texts to Recommend Peer Review Decisions (P19-1)
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| Challenge: | Existing peer review system is not straightforward and requires domain knowledge, expertise, and intelligence of human reviewers, which is somewhat elusive with the current state of AI. |
| Approach: | They propose to use peer review texts to predict acceptance or rejection of a manuscript based on reviewer sentiment. |
| Outcome: | The proposed deep neural architecture achieves significant performance improvement over baselines (29% error reduction) in a recently released dataset of peer reviews. |
Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion (P19-1)
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| Challenge: | Existing speech recognition systems are built at individual, isolated utterance level to make building systems computationally feasible. |
| Approach: | They propose to use text-based external word and/or sentence embeddings to integrate conversational context information into a single neural network model. |
| Outcome: | The proposed model outperforms standard end-to-end speech recognition models on the Switchboard conversational speech corpus and improves word error rate with better conversational-context representation. |
Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection (P19-1)
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| Challenge: | Past work in personal health mention detection uses classification-based methods with human-engineered features or word embedding-based features. |
| Approach: | They propose to combine a pipeline-based and a feature augmentation-based approach to combine personal health mention detection with figurative usage detection to improve the accuracy of the prediction. |
| Outcome: | The proposed method improves the F-score of personal health mention detection by 2.21% over the pipeline-based approach and feature augmentation-based approaches. |
Complex Word Identification as a Sequence Labelling Task (P19-1)
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| Challenge: | Complex Word Identification (CWI) is a crucial first step in a simplification pipeline. |
| Approach: | They propose a system that performs CWI in context without extensive feature engineering and outperforms state-of-the-art systems on this task. |
| Outcome: | The proposed system outperforms state-of-the-art systems on complex word identification. |
Neural News Recommendation with Topic-Aware News Representation (P19-1)
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| Challenge: | Existing methods for learning accurate news representations do not consider topic information in news. |
| Approach: | They propose a neural news recommendation approach with topic-aware news representations using CNN networks and attention networks to select important words. |
| Outcome: | The proposed approach is based on a topic-aware news encoder and user encoder. |
Poetry to Prose Conversion in Sanskrit as a Linearisation Task: A Case for Low-Resource Languages (P19-1)
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| Challenge: | Obtaining the proper word ordering, called as the prose ordering, from a verse is often considered a task which requires linguistic expertise. |
| Approach: | They propose a word ordering (linearisation) task that ignores the word arrangement at the verse side. |
| Outcome: | The proposed model outperforms current models in word ordering for the translation task in Sanskrit. |
Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions (P19-1)
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Amirreza Shirani, Franck Dernoncourt, Paul Asente, Nedim Lipka, Seokhwan Kim, Jose Echevarria, Thamar Solorio
| Challenge: | Visual communication relies heavily on images and short texts to grab a viewer's attention and convey a message in the most efficient way. |
| Approach: | They propose a model that employs end-to-end label distribution learning on crowd-sourced data and predicts a selection distribution, capturing the inter-subjectivity and ambiguity of the input. |
| Outcome: | The proposed model captures the inter-subjectivity and ambiguity of the input and can be transformed to single-label learning by mapping labels to absolute labels via majority voting. |
Rumor Detection by Exploiting User Credibility Information, Attention and Multi-task Learning (P19-1)
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| Challenge: | Social media platforms do not always pose authentic information, and rumors spread fear or hate. |
| Approach: | They propose a new multi-task learning approach for rumor detection and stance classification tasks. |
| Outcome: | The proposed model outperforms the state-of-the-art rumor detection approaches on two datasets. |
Context-specific Language Modeling for Human Trafficking Detection from Online Advertisements (P19-1)
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Saeideh Shahrokh Esfahani, Michael J. Cafarella, Maziyar Baran Pouyan, Gregory DeAngelo, Elena Eneva, Andy E. Fano
| Challenge: | Human trafficking is a worldwide crisis. |
| Approach: | They propose a method to detect trafficking ads on online sites using natural language processing using a pre-trained textual language model. |
| Outcome: | The proposed classifier significantly outperforms any single feature set alone. |
Self-Attentional Models for Lattice Inputs (P19-1)
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| Challenge: | Existing work has extended recurrent neural networks to model lattice inputs but these models suffer from slow computation speeds. |
| Approach: | They propose to extend the paradigm of self-attention to handle lattice inputs by adding probabilistic reachability masks that incorporate latticae structure into the model and support lattics if available. |
| Outcome: | The proposed model outperforms baseline models while being much faster to compute than previous models. |
When a Good Translation is Wrong in Context: Context-Aware Machine Translation Improves on Deixis, Ellipsis, and Lexical Cohesion (P19-1)
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| Challenge: | et al., 2018: translation errors due to the lack of extra-sentential context are becoming more and more noticeable among otherwise adequate translations. |
| Approach: | They propose a context-aware translation model that uses sentence-level data to identify inconsistencies . standard metrics are not sensitive to improvements in consistency in document-level translations . |
| Outcome: | The proposed model shows major gains over baseline without sacrificing performance . standard metrics are not sensitive to improvements in document-level translations . |
A Compact and Language-Sensitive Multilingual Translation Method (P19-1)
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| Challenge: | Existing paradigms for multilingual neural machine translation do not make full use of language commonality and parameter sharing. |
| Approach: | They propose a multilingual neural machine translation paradigm with one encoder-decoder model that makes full use of language commonality and parameter sharing. |
| Outcome: | The proposed method outperforms strong standard multilingual translation systems on WMT and IWSLT datasets. |
Unsupervised Parallel Sentence Extraction with Parallel Segment Detection Helps Machine Translation (P19-1)
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| Challenge: | Recent advances in unsupervised bilingual word embeddings make it possible to mine parallel sentences from comparable corpora. |
| Approach: | They propose a strong unsupervised system for parallel sentence mining based on cosine similarities of source and target words . they show that parallel sentences mined from real-life sources improve unsupervised MT . |
| Outcome: | The proposed system improves unsupervised MT on three language pairs. |
Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation (P19-1)
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| Challenge: | Unsupervised bilingual word embedding (UBWE) has helped unsupervised neural machine translation (UNMT) achieve remarkable results in several language pairs. |
| Approach: | They propose two methods that train UNMT with UBWE agreement . they propose to use UBwe to initialize word embedding in UNMT . |
| Outcome: | The proposed methods outperform conventional methods on several language pairs. |
Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies (P19-1)
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| Challenge: | Existing approaches to transfer a pretrained NMT model to a new, unrelated language without shared vocabularies are limited to cognate languages. |
| Approach: | They propose to transfer a pretrained NMT model to a new, unrelated language without shared vocabularies by using cross-lingual word embedding and injecting artificial noises. |
| Outcome: | The proposed methods outperform multilingual joint training by a large margin in five low-resource translation tasks. |
Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations (P19-1)
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| Challenge: | Existing approaches to train a multilingual NMT model for low-resource languages are lacking in terms of number of supervised examples. |
| Approach: | They propose to use decoder pre-training and back-translation to solve the degeneracy problem by analyzing spurious correlations between source and decoded sentences. |
| Outcome: | The proposed methods show significant improvement over the pivot-based approach on three challenging multilingual datasets. |
Syntactically Supervised Transformers for Faster Neural Machine Translation (P19-1)
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| Challenge: | Standard decoders for neural machine translation generate a single token per timestep, which slows inference . a series of controlled experiments demonstrates that SynST decodes sentences 5x faster than the baseline autoregressive Transformer. |
| Approach: | They propose a syntactically supervised Transformer that generates all target tokens in one shot . synST is a variant of the Transformer architecture that autoregressively predicts a chunked parse tree . |
| Outcome: | The proposed method decodes sentences 5x faster than the baseline method on En-De and En-Fr datasets while achieving higher BLEU scores. |
Dynamically Composing Domain-Data Selection with Clean-Data Selection by “Co-Curricular Learning” for Neural Machine Translation (P19-1)
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| Challenge: | Existing studies focus separately on domain-data selection, clean-data selecting, or their static combination, leaving the dynamic interaction across them not explicitly examined. |
| Approach: | They propose a method to combine dynamic domain-data selection with dynamic clean-data selecting for transfer learning across both capabilities. |
| Outcome: | The proposed method performs well on two domains and shows the properties of the data scheduled by the co-curriculum. |
On the Word Alignment from Neural Machine Translation (P19-1)
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| Challenge: | Prior researches suggest that neural machine translation (NMT) captures word alignment through its attention mechanism, however, attention may fail to capture word alignment for some NMT models. |
| Approach: | They propose two methods to induce word alignment which are general and agnostic to specific NMT models. |
| Outcome: | The proposed methods induce much better word alignment than attention. |
Imitation Learning for Non-Autoregressive Neural Machine Translation (P19-1)
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| Challenge: | Existing non-autoregressive translation models lack parallel decoding, which is a bottleneck for NMT decoding. |
| Approach: | They propose a framework for non-autoregressive machine translation that emulates the autoregressive model by sampling sentence length in parallel. |
| Outcome: | The proposed model achieves 31.85 BLEU on WMT16 RoEn and 30.68 BLUE on IWSLT16 EnDe on the IWSLD16, WMT14 and WMT15 datasets. |
Monotonic Infinite Lookback Attention for Simultaneous Machine Translation (P19-1)
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Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey, Chung-Cheng Chiu, Semih Yavuz, Ruoming Pang, Wei Li, Colin Raffel
| Challenge: | Simultaneous machine translation begins to translate each source sentence before the source speaker has finished speaking, with applications to live and streaming scenarios. |
| Approach: | They propose a simultaneous translation system that learns an adaptive schedule with a neural machine translation model that attends over all source tokens read thus far. |
| Outcome: | The proposed system can achieve latency-quality trade-offs favorable to a proposed wait-k strategy for many latency values. |
Global Textual Relation Embedding for Relational Understanding (P19-1)
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| Challenge: | Existing methods to learn textual relation embeddings are lacking in large open-domain corpora. |
| Approach: | They propose to learn a general-purpose embedding of textual relations using a large dataset from Freebase. |
| Outcome: | The proposed embedding can facilitate downstream tasks requiring relational understanding of the text. |
Graph Neural Networks with Generated Parameters for Relation Extraction (P19-1)
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| Challenge: | Existing graph neural networks can only process multi-hop relational reasoning on pre-defined graphs and cannot be directly applied in natural language relational reasoning. |
| Approach: | They propose a graph neural network with generated parameters using natural language sentences as inputs. |
| Outcome: | The proposed model can process relational reasoning on graphs and in natural language processing tasks. |
Entity-Relation Extraction as Multi-Turn Question Answering (P19-1)
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| Challenge: | Identifying entities and their relations is the prerequisite of extracting structured knowledge from unstructured raw texts. |
| Approach: | They propose a new paradigm for the task of entity-relation extraction . they cast the task as a multi-turn question answering problem . |
| Outcome: | The proposed paradigm significantly outperforms previous best models on the ACE and CoNLL04 datasets. |
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data (P19-1)
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| Challenge: | Existing methods to perform relation extraction are feature-based or kernel-based, but the results of our study show that they can improve the performance of a baseline model with more than 10% absolute increase in F1-score. |
| Approach: | They propose a multi-task architecture which jointly trains a model to perform relation identification with cross-entropy loss and relation classification with ranking loss. |
| Outcome: | The proposed model outperforms the state-of-the-art models on ACE 2005 Chinese and English corpus and significantly improves the performance of a baseline model with more than 10% increase in F1-score. |
Joint Type Inference on Entities and Relations via Graph Convolutional Networks (P19-1)
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| Challenge: | a novel graph convolutional network (GCN) is proposed for the task of joint entity relation extraction. |
| Approach: | They propose a graph convolutional network running on an entity-relation bipartite graph . they propose combining two different methods to perform joint entity relation extraction . |
| Outcome: | The proposed model outperforms existing joint models in entity performance and is competitive with the state-of-the-art in relation performance. |
Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers (P19-1)
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| Challenge: | Existing approaches to extract multiple relations from a paragraph require multiple passes over the paragraph. |
| Approach: | They propose a method to extract multiple relations from a paragraph by encoding the paragraph only once. |
| Outcome: | The proposed approach can perform state-of-the-art on the benchmark ACE 2005. |
Unsupervised Information Extraction: Regularizing Discriminative Approaches with Relation Distribution Losses (P19-1)
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| Challenge: | Existing unsupervised relation extraction models are either generative or discriminative . however, they are hard to train without supervision and are unstable . |
| Approach: | They propose a skewness loss and distribution distance loss to improve the performance of discriminative based models. |
| Outcome: | The proposed models surpass current state-of-the-art on three different datasets. |
Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction (P19-1)
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| Challenge: | Current relation extraction methods suffer from noisy labels and incomplete knowledge base information. |
| Approach: | They propose a pre-trained language model that captures semantic and syntactic features and a significant amount of “common-sense” knowledge. |
| Outcome: | The proposed model achieves state-of-the-art AUC score of 0.422 on the NYT10 dataset and performs especially well at higher recall levels. |
ARNOR: Attention Regularization based Noise Reduction for Distant Supervision Relation Classification (P19-1)
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| Challenge: | Distant supervision is used for relation classification but it introduces noisy labels . a novel approach to distant supervision relation classification is proposed . |
| Approach: | They propose a framework for distant supervision relation classification using attention regularization and attention regularizing . they assume that a trustable relation label should be explained by the neural attention model . |
| Outcome: | The proposed framework improves on the NYT data and noise reduction effect over state-of-the-art methods. |
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction (P19-1)
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| Challenge: | GraphRel is an end-to-end relation extraction model that uses graph convolutional networks to learn named entities and relations. |
| Approach: | They propose a graph-based relation extraction model which uses graph convolutional networks to jointly learn named entities and relations. |
| Outcome: | The proposed model outperforms previous models on two public datasets: NYT and WebNLG. |
DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction (P19-1)
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| Challenge: | Existing methods for labeling relational facts require significant expert labor to write relation-specific patterns, which makes them too sophisticated to generalize quickly. |
| Approach: | They propose a neural pattern diagnosis framework that can summarize and refine relation-specific patterns with human experts in the loop. |
| Outcome: | The proposed framework can summarize and refine high-quality relational patterns from noise data with human experts in the loop. |
Multi-grained Named Entity Recognition (P19-1)
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| Challenge: | Existing approaches treat Named Entity Recognition (NER) as a sequence labeling task. |
| Approach: | They propose a framework for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested. |
| Outcome: | The proposed framework outperforms current state-of-the-art frameworks by 4.4% in terms of the F1 score among nested/non-overlapping NER tasks. |
ERNIE: Enhanced Language Representation with Informative Entities (P19-1)
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| Challenge: | Existing pre-trained language models rarely consider incorporating knowledge graphs (KGs) Existing models capture rich semantic patterns from plain text and can be fine-tuned to improve performance of NLP tasks. |
| Approach: | They propose to incorporate knowledge graphs into pre-trained language models to enhance language representation with external knowledge. |
| Outcome: | The proposed model can take full advantage of lexical, syntactic, and knowledge information simultaneously. |
Multi-Channel Graph Neural Network for Entity Alignment (P19-1)
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| Challenge: | Existing methods to learn alignment-oriented knowledge graph embeddings suffer from structural heterogeneity and limited seed alignments. |
| Approach: | They propose a multi-channel Graph Neural Network model to learn alignment-oriented knowledge graph embeddings by encoding two KGs via multiple channels. |
| Outcome: | The proposed model is expected to reconcile the structural differences of two KGs, and thus make better use of seed alignments. |
A Neural Multi-digraph Model for Chinese NER with Gazetteers (P19-1)
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| Challenge: | Existing approaches to incorporating gazetteers into NER systems rely on manually defined selection strategies or handcrafted templates, which may not lead to optimal effectiveness. |
| Approach: | They propose to use graph neural networks to automatically learn how to incorporate multiple gazetteers into an NER system by capturing the information that the gazetteer offers. |
| Outcome: | The proposed model outperforms existing methods on Chinese NER datasets while incorporating rich gazetteer information while resolving ambiguities. |
Improved Language Modeling by Decoding the Past (P19-1)
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| Challenge: | Existing methods to improve language modeling performance are based on regularized LSTMs with a large number of parameters and training time. |
| Approach: | They propose a method that decodes the last token in context using the predicted distribution of the next token. |
| Outcome: | The proposed method improves perplexity on the Penn Treebank dataset by 1.8 points and 2.3 points on the WikiText-2 datasets. |
Training Hybrid Language Models by Marginalizing over Segmentations (P19-1)
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| Challenge: | Statistical language modeling is the problem of estimating a probability distribution over text data. |
| Approach: | They propose to marginalize over the segmentations efficiently to compute the true probability of a sequence. |
| Outcome: | The proposed model marginalizes over the segmentations to compute the true probability of a sequence on three datasets comprising seven languages. |
Improving Neural Language Models by Segmenting, Attending, and Predicting the Future (P19-1)
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| Challenge: | Common language models typically predict the next word given a past context. |
| Approach: | They propose a method that aligns the given context and the following phrase . they define syntactic heights and phrase segmentation rules to enable it to learn . |
| Outcome: | The proposed model outperforms strong baseline models on Wikitext-103 dataset. |
Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks (P19-1)
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| Challenge: | Existing models for natural language processing are heavily parameterized and memory inefficient. |
| Approach: | They propose a series of lightweight and memory efficient neural architectures for NLP tasks . they propose quaternion algebra and hypercomplex spaces for computation . |
| Outcome: | The proposed models enable expressive inter-component interactions and significantly reduce parameter size without loss of performance. |
Sparse Sequence-to-Sequence Models (P19-1)
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| Challenge: | Sequence-to-sequence models are dense and assigning nonzero probability to implausible outputs. |
| Approach: | They propose a new family of -entmax transformations that includes softmax and sparsemax as particular cases and is sparser for any > 1 . they provide fast algorithms to evaluate these transformations and their gradients, which scale well for large vocabulary sizes. |
| Outcome: | The proposed models are able to produce sparse alignments and assign nonzero probability to short list of plausible outputs, sometimes rendering beam search exact. |
On the Robustness of Self-Attentive Models (P19-1)
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| Challenge: | Experimental results show that self-attentive neural models are more robust against adversarial perturbations compared to recurrent neural networks. |
| Approach: | They propose an adversarial attack algorithm that generates more natural adversarials . they propose to use the attention mechanism to learn a context-dependent representation . |
| Outcome: | The proposed attack algorithm generates more natural adversarial examples that could mislead models but not humans. |
Exact Hard Monotonic Attention for Character-Level Transduction (P19-1)
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| Challenge: | Neural sequence-to-sequence models with soft attention outperform monotonic models . current dominant method is the neural sequenceto-Sequency model with soft focus . |
| Approach: | They develop a hard attention sequence-to-sequence model that enforces strict monotonicity and learns alignment jointly. |
| Outcome: | The proposed model achieves state-of-the-art on grapheme-to-phoneme conversion and morphological inflection generation. |
A Lightweight Recurrent Network for Sequence Modeling (P19-1)
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| Challenge: | Recent studies show that recurrent networks suffer from severe computational inefficiency due to weak parallelization. |
| Approach: | They propose a lightweight recurrent network (LRN) that uses input and forget gates to handle long-range dependencies and gradient vanishing and explosion. |
| Outcome: | The proposed recurrent network yields the best running efficiency on six NLP tasks. |
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications (P19-1)
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| Challenge: | Existing approaches to generalize deep neural networks are datahungry and generalize poorly from small datasets. |
| Approach: | They propose an agreement score to evaluate routing processes at instance-level and an adaptive optimizer to enhance routing. |
| Outcome: | The proposed approach improves on two NLP tasks and in low-resource settings with few training instances. |
Soft Representation Learning for Sparse Transfer (P19-1)
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| Challenge: | Using adversarial training, we can “soft-code” shared and private spaces to avoid sparse sharing. |
| Approach: | They propose to use adversarial training to “soft-code” shared and private spaces to avoid the shared space gets too sparse. |
| Outcome: | The proposed architecture avoids sparse sharing of shared and private spaces, and also deals with low-quality input. |
Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization (P19-1)
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Paul Pu Liang, Zhun Liu, Yao-Hung Hubert Tsai, Qibin Zhao, Ruslan Salakhutdinov, Louis-Philippe Morency
| Challenge: | Existing methods to regularize multimodal data are imperfect due to imperfect modalities, missing entries or noise corruption. |
| Approach: | They propose a method to regularize multimodal data by tensor rank minimization . they use correlations between time and modalities to generate low-rank tenses . |
| Outcome: | The proposed model achieves good results across various levels of imperfection. |
Towards Lossless Encoding of Sentences (P19-1)
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| Challenge: | Existing methods for encoding text into lossless representations focus on performing well on downstream tasks and are unable to reconstruct original sequence from learned embedding. |
| Approach: | They propose a lossless method for encoding long sequences of texts into feature rich representations by recursive autoencoding. |
| Outcome: | The proposed method performs well on sentiment analysis and sentiment classification tasks. |
Open Vocabulary Learning for Neural Chinese Pinyin IME (P19-1)
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| Challenge: | Pinyin-to-character conversion is the core component of pinyin based Chinese input method engine (IME). |
| Approach: | They propose a neural P2C conversion model augmented by an online updated vocabulary to support open vocabulary learning during IME working. |
| Outcome: | The proposed model outperforms commercial IMEs and state-of-the-art models on standard corpus and true inputting history dataset in terms of multiple metrics and the online updated vocabulary helps it follow user inputting behavior. |
Using LSTMs to Assess the Obligatoriness of Phonological Distinctive Features for Phonotactic Learning (P19-1)
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| Challenge: | a recent study has shown that phonetic information is important for phonotactic learning . a feature-naive model outperforms the feature-aware one in terms of probability assigned to a held-out test set of words . |
| Approach: | They compare two recurrent neural network models of phonotactic learning to determine phonetic information in the form of phonological distinctive features. |
| Outcome: | The proposed model outperforms the one with access to distinctive features at the start of learning . the results suggest that distinctive features are not obligatory for learning phonotactic patterns . |
Better Character Language Modeling through Morphology (P19-1)
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| Challenge: | Inflected words benefit more from explicitly modeling morphology than uninflectes . morphological supervision is also used to augment character language models in low-resource languages . |
| Approach: | They add morphological supervision to character language models via multitasking to improve BPC performance across 24 languages even when morphology data and language modeling data are disjointed. |
| Outcome: | The addition improves performance even when morphology data and language modeling data are disjointed. |
Historical Text Normalization with Delayed Rewards (P19-1)
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| Challenge: | Recent work on a novel approach to historical text normalization has shown that policy gradient fine-tuning improves accuracy across languages. |
| Approach: | They propose to train sequence-to-sequence models with simple token-level log-likelihood with reinforcement learning to optimize for exact matches. |
| Outcome: | The proposed model outperforms phrase-based models in the evaluation metric for historical text normalization across languages. |
Stochastic Tokenization with a Language Model for Neural Text Classification (P19-1)
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| Challenge: | Sentences segmented with words or subwords can be difficult to perform text classification tasks. |
| Approach: | They propose a method to learn tokenization and text classification simultaneously to address these problems. |
| Outcome: | The proposed method improves on sentiment analysis in Japanese and Chinese using tokenization and text classification models. |
Mitigating Gender Bias in Natural Language Processing: Literature Review (P19-1)
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Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang
| Challenge: | NLP models propagate and may even amplify gender bias found in text corpora . methods to mitigate gender bias in NLP are relatively nascent . |
| Approach: | They propose to analyze gender bias based on four forms of representation bias and discuss the advantages and drawbacks of existing gender debiasing methods. |
| Outcome: | The proposed methods are based on four forms of representation bias and have advantages and drawbacks. |
Gender-preserving Debiasing for Pre-trained Word Embeddings (P19-1)
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| Challenge: | Existing methods for debiasing word embeddings have shown discriminative biases . word embeds learnt from social media have shown to encode racist, offensive and discriminative language usage. |
| Approach: | They propose a method that preserves gender-related information while removing stereotypical gender biases from pre-trained word embeddings. |
| Outcome: | The proposed method preserves gender-related information while removing stereotypical discriminative gender biases from pre-trained word embeddings. |
Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology (P19-1)
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| Challenge: | Gender stereotypes are manifest in most of the world's languages and are consequently propagated or amplified by NLP systems. |
| Approach: | They propose a method for converting between masculine-inflected and feminine-infflectes sentences in morphologically rich languages to reduce gender stereotyping by a factor of 2.5 without any sacrifice to grammaticality. |
| Outcome: | The proposed approach reduces gender stereotyping by 2.5 without any sacrifice to grammaticality. |
A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings (P19-1)
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| Challenge: | Word embeddings suffer from unintended demographic biases, a new study shows . word embedders can cause downstream NLP systems to be unfair, the authors argue . |
| Approach: | They propose a metric to evaluate the fairness of word embeddings via the relative negative sentiment associated with demographic identity terms from various protected groups. |
| Outcome: | The proposed metric measures fairness in word embeddings via the relative negative sentiment associated with demographic identity terms from various protected groups. |
The Risk of Racial Bias in Hate Speech Detection (P19-1)
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| Challenge: | Annotators’ insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations. |
| Approach: | They propose *dialect* and *race priming* as ways to reduce the racial bias in hate speech detection models by detecting differences in dialects in annotated tweets. |
| Outcome: | The proposed models acquire and propagate these biases, such that AAE tweets and tweets by self-identified African Americans are up to two times more likely to be labelled as offensive compared to others. |
Evaluating Gender Bias in Machine Translation (P19-1)
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| Challenge: | Using morphological analysis, we find that MT models exhibit gender-biased translation errors when training data encode stereotypes not relevant for the task. |
| Approach: | They propose an automatic gender bias evaluation method for eight target languages with grammatical gender based on morphological analysis. |
| Outcome: | The proposed method is based on two recent coreference resolution datasets composed of English sentences cast participants into non-stereotypical gender roles. |
LSTMEmbed: Learning Word and Sense Representations from a Large Semantically Annotated Corpus with Long Short-Term Memories (P19-1)
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| Challenge: | Recent work has focused on vector representations which capture different meanings, i.e., senses, of words. |
| Approach: | They propose a bidirectional LSTM model which learns word senses from semantically annotated corpora by focusing on word order. |
| Outcome: | The proposed model achieves state-of-the-art on the SemEval-2014 word-to-sense similarity task and is available online at http://lcl.uniroma1.it/LSTMEmbed. |
Understanding Undesirable Word Embedding Associations (P19-1)
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| Challenge: | Word embeddings are often criticized for capturing undesirable word associations such as gender stereotypes. |
| Approach: | They propose to use subspace projection to debias vectors post hoc using a model that implicitly does matrix factorization to debunk gender bias. |
| Outcome: | The proposed test overestimates gender bias in word embeddings by using subspace projection, a method that is widely used in training. |
Unsupervised Discovery of Gendered Language through Latent-Variable Modeling (P19-1)
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| Challenge: | a recent study has focused on the ways in which language is gendered . positive adjectives used to describe women are more often related to their bodies . |
| Approach: | They propose a model that models adjective choice and its sentiment given the natural gender of a head noun. |
| Outcome: | The proposed model shows that positive adjectives used to describe women are more often related to their bodies than positive adjective words used to explain men. |
Topic Sensitive Attention on Generic Corpora Corrects Sense Bias in Pretrained Embeddings (P19-1)
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| Challenge: | Existing methods to adapt pretrained embeddings to a large corpus are limited and do not provide sufficient quality. |
| Approach: | They propose to use a small corpus D_T to pretrain embeddings that accurately capture the sense of words in a limited set of focused topics. |
| Outcome: | The proposed embeddings capture the sense of words in a topic in spite of the limited size of the corpus D_T. |
SphereRE: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings (P19-1)
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| Challenge: | Lexical relations are relations between terms in lexicons. |
| Approach: | They propose a neural representation learning model to distinguish lexical relations among term pairs based on hyperspherical relation embeddings. |
| Outcome: | The proposed model outperforms state-of-the-art models on several benchmarks. |
Multilingual Factor Analysis (P19-1)
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| Challenge: | Existing methods for multilingual word embeddings are based on the observation that word embeds exhibit similar structures across languages. |
| Approach: | They propose a latent variable-based model that fits a multilingual dictionary to learn multilingual word representations offline. |
| Outcome: | The proposed model is robust to noise in the embedding space making it suitable for distributed representations learned from noisy corpora. |
Meaning to Form: Measuring Systematicity as Information (P19-1)
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| Challenge: | A longstanding debate in semiotics centers on the relationship between linguistic signs and their corresponding semantics: is there an arbitrary relationship between word forms and their meaning, or does some systematic phenomenon pervade? |
| Approach: | They propose to quantify the systematicity of the sign using mutual information and recurrent neural networks to examine 106 languages. |
| Outcome: | The proposed model reduces entropy in a word form conditioned on its semantic representation and recovers English examples of systematic affixes. |
Learning Morphosyntactic Analyzers from the Bible via Iterative Annotation Projection across 26 Languages (P19-1)
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| Challenge: | Currently, computational tools for low-resource languages are limited by a lack of supervised training data. |
| Approach: | They propose to use English taggers and parsers to project morphological information onto translations of the Bible in 26 different test languages. |
| Outcome: | The proposed method reduces lemmatization and morphological analysis over a strong initial system. |
Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling (P19-1)
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| Challenge: | Morphological tagging is challenging for morphologically rich languages due to the large combined target space and the need for more training data to minimize model sparsity. |
| Approach: | They propose to use multitask learning and adversarial training to address morphological richness and dialectal variations in the context of full morphology. |
| Outcome: | The proposed model achieves state-of-the-art for two dialectal variants: Modern Standard Arabic (high-resource “dialect”) and Egyptian Arabic (low-resourced dialect). |
Neural Machine Translation with Reordering Embeddings (P19-1)
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| Challenge: | Existing work exploits the reordering information in neural machine translation . experimental results show that the proposed methods can significantly improve the performance of the transformer translation system. |
| Approach: | They propose a reordering mechanism to learn the re ordering embedding of a word based on contextual information and stack them together with self-attention networks to learn sentence representation for machine translation. |
| Outcome: | The proposed method improves translation performance on English-to-German, NIST Chinese-to English, and WAT Japanese-toEnglish translation tasks. |
Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation (P19-1)
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| Challenge: | Several configurations are tested on the DGT-TM data set for the language directions English into Dutch (ENNL) and English into Hungarian (ENHU). |
| Approach: | They propose and test two methods for augmenting NMT training data with fuzzy TM matches by concatenation. |
| Outcome: | The proposed method improves on the DGT-TM data set for two language pairs and is easy to implement. |
Learning Deep Transformer Models for Machine Translation (P19-1)
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| Challenge: | Neural machine translation models have advanced the previous state-of-the-art by learning mappings between sequences via neural networks and attention mechanisms. |
| Approach: | They propose to use layer normalization to pass the combination of previous layers to the next layer to improve the model. |
| Outcome: | The proposed model outperforms the shallow Transformer-Big/Base baseline model on English-German and Chinese-English tasks by 0.4-2.4 BLEU points. |
Generating Diverse Translations with Sentence Codes (P19-1)
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| Challenge: | Existing methods to generate diverse translations use different sentence structures . Xu et al., 2018: generating multiple valid translations with high diversity is difficult . |
| Approach: | They propose to use sentence codes to condition the sentence generation to obtain diverse translations . they propose to sample multiple candidates, each of which conditioned on a unique code . |
| Outcome: | The proposed method generates paraphrase translations with drastically different structures . the proposed method can be easily adopted to existing translation systems . |
Self-Supervised Neural Machine Translation (P19-1)
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| Challenge: | Neural machine translation (NMT) methods relied on the availability of high-quality parallel corpora. |
| Approach: | They propose a method where an emergent NMT system is used for selecting training data and learning internal NMT representations. |
| Outcome: | The proposed method achieves BLEU scores of 29.21 (en2fr) and 27.36 (fr2en) on newstest2014 using English and French Wikipedia data for training. |
Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation (P19-1)
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| Challenge: | Previous work on end-to-end translation from speech uses frame-level features as speech representations, which creates longer, sparser sequences than text. |
| Approach: | They propose a method to generate compressed phoneme-like speech representations that generate shorter, higher-level source sequences for translation. |
| Outcome: | The proposed method improves translation performance by 5 BLEU on high and low resource languages and reduces training time by 60%. |
Visually Grounded Neural Syntax Acquisition (P19-1)
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| Challenge: | a visually grounded neural syntax learner is an approach for learning syntactic representations without any supervision. |
| Approach: | They propose a visually grounded neural syntax learner that acquires syntax by looking at images and reading captions. |
| Outcome: | The proposed model outperforms unsupervised approaches on the MSCOCO data set . it is more stable with choice of initialization and amount of training data, the authors show . |
Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation (P19-1)
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| Challenge: | Existing metrics for vision-and-language navigation focus on goal completion rather than the sequence of actions corresponding to the instructions. |
| Approach: | They propose to use a room-to-room dataset to measure the length of instruction followed by agents. |
| Outcome: | The proposed metric outperforms existing metrics for Room-to-Room tasks because it is direct-to goal shortest. |
Expressing Visual Relationships via Language (P19-1)
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| Challenge: | Current studies on image captioning focus on single image, but there are no effective models for generating relational captions for two images. |
| Approach: | They propose a language-guided image editing dataset that contains real image pairs with corresponding editing instructions. |
| Outcome: | The proposed model outperforms baseline and existing methods on two datasets. |
Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in Video (P19-1)
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| Challenge: | Existing techniques for weakly-supervised spatio-temporally grounding natural sentence in video are lacking . |
| Approach: | They propose a weakly-supervised task for spatially grounding sentences in video . they extract instances from video and encode them using attentive interactor . results demonstrate superiority of their proposed task over baseline approaches . |
| Outcome: | The proposed model outperforms baseline approaches in a weakly-supervised task . it can characterize reliable instance-sentence pairs and penalize unreliable ones . |
The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue (P19-1)
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| Challenge: | Using the PhotoBook dataset, we investigate shared dialogue history accumulating during conversation . human interlocutors are known to collaboratively establish a shared repository of mutual information during a conversation - this common ground is then used to optimise understanding and communication efficiency. |
| Approach: | They propose a data-collection task formulated as a collaborative game prompting two online participants to refer to images utilising both their visual context and previously established referring expressions. |
| Outcome: | The proposed model takes into account shared information accumulated in a reference chain and is important to resolve later descriptions. |
Continual and Multi-Task Architecture Search (P19-1)
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| Challenge: | Recent studies have shown that architecture search can improve performance on language modeling and image classification tasks with reasonable training speed. |
| Approach: | They propose a continual architecture search approach that continually evolves the model parameters during sequential training of several tasks without losing performance on previously learned tasks. |
| Outcome: | The proposed approach improves language modeling and image classification with reasonable training speed and a weight-sharing strategy. |
Semi-supervised Stochastic Multi-Domain Learning using Variational Inference (P19-1)
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| Challenge: | Supervised NLP models rely on large collections of text which closely resemble intended testing setting. however, data is often messy, with domain labels not always available, or providing limited information about the style and genre of text. |
| Approach: | They propose a method to distill the important domain signal as part of a multi-domain learning system using a latent variable model. |
| Outcome: | The proposed model improves performance over benchmark domain adaptation methods . text corpora are often collated from several different sources, including news, literature, microblogs, and web crawls . |
Boosting Entity Linking Performance by Leveraging Unlabeled Documents (P19-1)
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| Challenge: | a new approach to entity linking relies on unlabeled documents and Wikipedia . a supervised approach uses only natural information, such as unlabed documents . |
| Approach: | They propose a method which exploits only naturally occurring information . they construct a high recall list of candidate entities for each mention in an unlabeled document . |
| Outcome: | The proposed model outperforms fully-supervised state-of-the-art systems on standard test sets. |
Pre-Learning Environment Representations for Data-Efficient Neural Instruction Following (P19-1)
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| Challenge: | Using logical forms, neural networks can sometimes require orders of magnitude more data to map from natural language instructions to state transitions (actions) |
| Approach: | They propose to map from natural language instructions to state transitions (actions) they augment a baseline learner with an initial environment-learning phase that uses observations of language-free state transition to induce a suitable latent representation of actions before processing the instruction-following training data. |
| Outcome: | The proposed model improves performance over systems whose representations are learned from limited instructional data alone. |
Reinforced Training Data Selection for Domain Adaptation (P19-1)
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| Challenge: | Existing approaches to learn domains with massive data are not easy to implement and require a predefined threshold. |
| Approach: | They propose a framework that searches for training instances relevant to the target domain and learns better representations for them. |
| Outcome: | The proposed framework is effective in data selection and representation, but generalized to accommodate different NLP tasks. |
Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine Decoding (P19-1)
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| Challenge: | Existing methods for review generation lack topical and syntactic characteristics of natural languages. |
| Approach: | They propose a review generation model that uses aspect semantics, syntactic sketch, and context information to generate a sentence and corresponding words. |
| Outcome: | The proposed model can generate long and informative review text for users given a product and her/his rating on it. |
PaperRobot: Incremental Draft Generation of Scientific Ideas (P19-1)
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| Challenge: | a paper robot can read existing papers and create new nodes or links in the knowledge graphs. |
| Approach: | They propose to automate the creation of new ideas by predicting links from the background KGs. |
| Outcome: | The proposed paper automates three tasks: read existing papers, create new ideas, predict links . the paper generated abstracts, conclusion and future work sections, and new titles are chosen over human-written ones up to 30%, 24% and 12% of the time. |
Rhetorically Controlled Encoder-Decoder for Modern Chinese Poetry Generation (P19-1)
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| Challenge: | Rhetoric is a vital element in modern Chinese poetry, and plays an essential role in improving its aesthetics. however, to date, it has not been considered in research on automatic poetry generation. |
| Approach: | They propose a rhetorically controlled encoder-decoder for modern Chinese poetry generation . their model captures various rhetorical patterns in an encoder and incorporates mixtures . |
| Outcome: | The proposed model outperforms state-of-the-art methods in terms of fluency, coherence, meaningfulness, and rhetorical aesthetics. |
Enhancing Topic-to-Essay Generation with External Commonsense Knowledge (P19-1)
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| Challenge: | Existing methods for topic-to-essay generation are insufficient for generating novel, diverse, and topic-consistent paragraph-level text with a set of topics. |
| Approach: | They propose to integrate commonsense from external knowledge base into the generator through dynamic memory mechanism and adversarial training to further improve topic-consistency. |
| Outcome: | The proposed task is more novel, diverse, and topic-consistent than existing methods in terms of both automatic and human evaluation. |
Towards Fine-grained Text Sentiment Transfer (P19-1)
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| Challenge: | Existing methods for fine-grained text sentiment transfer only reverse the sentiment polarity of text, but they lack a robust and parallel learning algorithm. |
| Approach: | They propose a novel fine-grained text sentiment transfer task that revises a sequence to satisfy a given sentiment intensity while preserving the original semantic content. |
| Outcome: | The proposed model outperforms existing methods by a large margin in automatic evaluation and human evaluation. |
Data-to-text Generation with Entity Modeling (P19-1)
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| Challenge: | Recent approaches to data-to-text generation have shown great promise thanks to the use of large-scale datasets and the application of neural network architectures which are trained end-to end. |
| Approach: | They propose an entity-centric neural architecture for data-to-text generation which uses hierarchical attention to create entity-specific representations which are dynamically updated. |
| Outcome: | The proposed model outperforms baselines in automatic and human evaluation on the RotoWire benchmark and a five-times larger dataset on the baseball domain. |
Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation (P19-1)
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| Challenge: | Existing generative methods overlook grammatical structure or make factual mistakes in generated texts. |
| Approach: | They propose a template-based method to ensure the readability of generated type descriptions . they also propose measurable metrics to measure the readibility of the generated type description . |
| Outcome: | The proposed method improves substantially compared with baselines and achieves state-of-the-art performance on both datasets. |
Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation (P19-1)
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| Challenge: | Existing methods for table-to-text generation use encoder-decoder framework, but lack of large parallel data is a problem for many domains. |
| Approach: | They propose a model to separate table-to-text generation into two stages: key fact prediction and surface realization. |
| Outcome: | The proposed model achieves 27.34 BLEU score with only 1,000 parallel data, while the baseline model only achieves 9.71 BLUE score. |
Unsupervised Neural Text Simplification (P19-1)
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| Challenge: | Existing unsupervised methods for text simplification are limited to unlabeled text . paper aims to improve the performance of unsupervised systems by incorporating labeled pairs . |
| Approach: | They propose to use unlabeled text to train a neural text simplification framework . they propose to add a pair of attentional-decoders to the framework to improve performance . |
| Outcome: | The proposed model outperforms existing supervised methods on public test data. |
Syntax-Infused Variational Autoencoder for Text Generation (P19-1)
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| Challenge: | Experimental results demonstrate the generative superiority of SIVAE on both reconstruction and targeted syntactic evaluations. |
| Approach: | They propose a syntax-infused variational autoencoder that integrates sentences with their syntactic trees to improve the grammar of generated sentences. |
| Outcome: | The proposed model improves the grammar of generated sentences by integrating sentences with syntactic trees. |
Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models (P19-1)
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| Challenge: | Variational autoencoders (VAEs) have received much attention as an end-to-end architecture for text generation with latent variables. |
| Approach: | They propose to leverage several multi-level structures to learn a variational autoencoder model for generating long, and coherent text. |
| Outcome: | The proposed model produces more coherent and less repetitive long text compared to baselines and mitigates posterior collapse issue. |
Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization (P19-1)
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| Challenge: | Semantic parsing aims to transform natural language utterances into formal meaning representations (MRs) whereas an NL generator achieves the reverse, the two tasks are often studied separately. |
| Approach: | They propose a method of dual information maximization to regularize the learning process by matching the joint distributions of p and q of NLs. |
| Outcome: | The proposed method empirically maximizes the variational lower bounds of expected joint distributions of NL and MRs. |
Learning to Select, Track, and Generate for Data-to-Text (P19-1)
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Hayate Iso, Yui Uehara, Tatsuya Ishigaki, Hiroshi Noji, Eiji Aramaki, Ichiro Kobayashi, Yusuke Miyao, Naoaki Okazaki, Hiroya Takamura
| Challenge: | Existing models often refer to the same data record multiple times. |
| Approach: | They propose a data-to-text generation model with two modules, one for tracking and the other for text generation. |
| Outcome: | The proposed model outperforms existing models even without writer information in all evaluation metrics and contributes to content planning and surface realization. |
Reinforced Dynamic Reasoning for Conversational Question Generation (P19-1)
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| Challenge: | Empirical results on the recently released CoQA dataset demonstrate the effectiveness of our method . large-scale highquality conversational question answering datasets such as CoQA and QuAC can help train models to answer sequential questions. |
| Approach: | They propose a task called Conversational Question Generation which generates a question based on a passage and a conversation history to generate the next question. |
| Outcome: | The proposed method is based on a question-answering style conversation dataset . it can be used to generate meaningful questions on QA and SQuAD datasets . |
TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks (P19-1)
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| Challenge: | Currently, no large-scale training data is available for the task of scientific paper summarization. |
| Approach: | They propose a method that automatically generates scientific paper summaries by utilizing videos of scientific conferences. |
| Outcome: | The proposed model performs similar to models trained on a dataset of summaries created manually. |
Improving Abstractive Document Summarization with Salient Information Modeling (P19-1)
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| Challenge: | Abstractive document summarization is a task of natural language generation which generates fluent summaries with salient information automatically. |
| Approach: | They propose to incorporate a Gaussian focal bias on attention scores into an encoder to enhance the perception of local context and to distinguish salient information precisely. |
| Outcome: | The proposed framework outperforms state-of-the-art models on the CNN/Daily Mail benchmark and is based on a focus-attention mechanism and two new extensions. |
Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking (P19-1)
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| Challenge: | Currently, unsupervised summarization is widely used for product reviews on E-commerce websites. |
| Approach: | They propose an unsupervised model that learns the latent discourse tree without an external parser and generates a concise summary. |
| Outcome: | The proposed model outperforms other unsupervised approaches for relatively long reviews and is competitive with or better than supervised models. |
BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization (P19-1)
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| Challenge: | Abstractive summarization models are limited in size and noisy training data. |
| Approach: | They propose a bi-directional selective encoding with template model which leverages template from training data to softly select key information from each source article to guide its summarization process. |
| Outcome: | The proposed model improves the summarization performance significantly on a standard summarizing dataset. |
Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards (P19-1)
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| Challenge: | Existing generative models generate too few keyphrases, but they often generate too many . et al. (2017) propose a reinforcement learning approach for keyphrase generation . |
| Approach: | They propose a reinforcement learning approach that encourages a model to generate sufficient keyphrases with an adaptive reward function. |
| Outcome: | The proposed method improves state-of-the-art generative models with conventional and new evaluation methods on real-world datasets. |
Scoring Sentence Singletons and Pairs for Abstractive Summarization (P19-1)
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Logan Lebanoff, Kaiqiang Song, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, Fei Liu
| Challenge: | Existing methods for summarizing content from single sentences are inadequately understood. |
| Approach: | They propose to combine singletons and pairs to create a summarizing sentence . they use a dataset of human-written abstracts to examine human-writing methods . |
| Outcome: | The proposed framework is based on human-written abstracts from three large datasets. |
Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization (P19-1)
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| Challenge: | Existing models for extractive summarization of meetings are unfocused and lack content coverage. |
| Approach: | They propose a multi-modal hierarchical attention model that prioritizes segmentation and summarization . they propose to use multi-level hierarchies to narrow down the focus into topically-relevant segments . |
| Outcome: | The proposed model outperforms the state-of-the-art with BLEU and ROUGE measures. |
Adversarial Domain Adaptation Using Artificial Titles for Abstractive Title Generation (P19-1)
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| Challenge: | Obtaining good quality labeled data can be difficult and expensive for abstractive summarization models . authors propose the use of artificial titles for unlabeled target documents . |
| Approach: | They propose to use artificial titles and sequential training to capture grammatical style of unlabeled target domains to adapt to/from news articles and Stack Exchange posts. |
| Outcome: | The proposed techniques can boost performance for unsupervised adaptation and fine-tuning with limited target data. |
BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization (P19-1)
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| Challenge: | Existing text summarization datasets are compiled from news articles, where summary-worthy content often appears in the beginning of input articles. |
| Approach: | They present a novel dataset, BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. |
| Outcome: | The proposed dataset is compared with existing summarization datasets and demonstrates that salient content is evenly distributed in the input. |
Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference (P19-1)
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| Challenge: | Recent advances on abstractive summarization have led to fluent summaries, but factual errors in generated summary still severely limit their use in practice. |
| Approach: | They evaluate summaries produced by state-of-the-art models via crowdsourcing and show that factual errors occur frequently. |
| Outcome: | The proposed models can detect errors and reduce them by reranking alternative summaries. |
Self-Supervised Learning for Contextualized Extractive Summarization (P19-1)
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| Challenge: | Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss . previous work builds an end-to-end system to learn to choose sentences without explicitly modeling document context . |
| Approach: | They propose three auxiliary pre-training tasks that learn to capture the document context in a self-supervised fashion. |
| Outcome: | The proposed models outperform existing models on a CNN/DM dataset. |
On the Summarization of Consumer Health Questions (P19-1)
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| Challenge: | Question understanding is one of the main challenges in question answering. |
| Approach: | They propose to use semantic augmentation to augment question datasets to improve their performance. |
| Outcome: | The proposed model outperforms sequence-to-sequence attentional models on the medical question summarization task with a ROUGE-1 score of 44.16%. |
Unsupervised Rewriter for Multi-Sentence Compression (P19-1)
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| Challenge: | Multi-sentence compression aims to generate a grammatical but reduced compression from multiple input sentences while retaining key information. |
| Approach: | They propose a neural rewriter for multi-sentence compression that does not need any parallel corpus. |
| Outcome: | Empirical studies show that the proposed approach achieves comparable results upon automatic evaluation and improves the grammaticality of compression based on human evaluation. |
Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text (P19-1)
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| Challenge: | Experimental results on 3 popular datasets demonstrate the effectiveness of our approach. |
| Approach: | They propose a network to solve the inference problem by decomposing text into a series of attention-based reasoning steps. |
| Outcome: | The proposed network can be used to understand the meanings of given text to answer questions. |
Token-level Dynamic Self-Attention Network for Multi-Passage Reading Comprehension (P19-1)
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| Challenge: | Multi-passage reading comprehension requires the ability to combine cross-passages information and reason over multiple passages to infer the answer. |
| Approach: | They propose a Dynamic Self-attention Network (DynSAN) which processes cross-passage information at token-level and meanwhile avoids substantial computational costs. |
| Outcome: | The proposed model achieves state-of-the-art performance on the SearchQA, Quasar-T and WikiHop datasets and further ablation validates the effectiveness of its components. |
Explicit Utilization of General Knowledge in Machine Reading Comprehension (P19-1)
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| Challenge: | Existing MRC models are unable to integrate general knowledge with human knowledge. |
| Approach: | They propose a data enrichment method which uses WordNet to extract inter-word semantic connections as general knowledge from each given passage-question pair. |
| Outcome: | The proposed model outperforms state-of-the-art models and is robust to noise. |
Multi-style Generative Reading Comprehension (P19-1)
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Kyosuke Nishida, Itsumi Saito, Kosuke Nishida, Kazutoshi Shinoda, Atsushi Otsuka, Hisako Asano, Junji Tomita
| Challenge: | Current studies on generative reading comprehension (RC) focus on extracting an answer span from textual evidence and natural language generation (NLG). |
| Approach: | They propose a multi-style abstractive summarization model for question answering called Masque. |
| Outcome: | The proposed model achieves state-of-the-art performance on the Q&A and Q& A + NLG tasks of MS MARCO and NarrativeQA. |
Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension (P19-1)
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| Challenge: | Existing approaches to answer reading comprehension tasks are inefficient since the input is re-encoded within each module. |
| Approach: | They propose a unified question answering model that combines context retrieving, reading comprehension, and answer reranking to predict the final answer. |
| Outcome: | The proposed model outperforms the baseline model and achieves state-of-the-art results on two versions of TriviaQA and two variants of SQuAD. |
Multi-Hop Paragraph Retrieval for Open-Domain Question Answering (P19-1)
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| Challenge: | Existing methods for textual question answering are capable of outperforming humans on certain tasks. |
| Approach: | They propose a method for retrieving multiple supporting paragraphs from a large knowledge base. |
| Outcome: | The proposed method achieves state-of-the-art over two well-known datasets, SQuAD-Open and HotpotQA, which serve as benchmarks for the proposed method. |
E3: Entailment-driven Extracting and Editing for Conversational Machine Reading (P19-1)
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| Challenge: | Conversational machine reading systems help users answer high-level questions when they do not know the exact rules by which the decision is made. |
| Approach: | They propose a conversational machine reading model that extracts a set of decision rules from a procedural text which the system must read to figure out what to ask the user. |
| Outcome: | The proposed model outperforms existing systems and a BERT-based baseline on the ShARC conversational machine reading dataset and provides an explainable alternative to prior work. |
Generating Question-Answer Hierarchies (P19-1)
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| Challenge: | a novel text generation task uses reading comprehension datasets to generate a hierarchy of question-answer pairs . users can click on high-level questions to reveal related but more specific questions . |
| Approach: | They propose a text-generating task which converts a document into a hierarchy of question-answer pairs . users can click on high-level questions to reveal related but more specific questions . they then use the specificity-labeled reading comprehension dataset to generate the hierarchy . |
| Outcome: | The proposed system can be used to generate a hierarchy of question-answer pairs based on reading comprehension datasets. |
Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction (P19-1)
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Kosuke Nishida, Kyosuke Nishida, Masaaki Nagata, Atsushi Otsuka, Itsumi Saito, Hisako Asano, Junji Tomita
| Challenge: | Question answering (QA) using textual sources for purposes such as reading comprehension has attracted much attention. |
| Approach: | They propose a Query Focused Extractor model for evidence extraction and multi-task learning with the QA model. |
| Outcome: | The proposed model achieves state-of-the-art evidence extraction score on hotpotQA and FEVER, which is a recognizing textual entailment task on a large textual database. |
Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension (P19-1)
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| Challenge: | Recent results show pre-trained language models (LMs) can improve machine reading comprehension (MRC) Experimental results indicate that KT-NET offers significant and consistent improvements over BERT . |
| Approach: | They propose a method that leverages external knowledge bases to improve machine reading comprehension (MRC) KT-NET employs an attention mechanism to select desired knowledge from KBs and fuses selected knowledge with BERT to enable context- and knowledge-aware predictions. |
| Outcome: | The proposed model outperforms baseline models on ReCoRD and SQuAD1.1 benchmarks and ranks 1st on the ReCoDR and SQUAD1.1 leaderboards. |
XQA: A Cross-lingual Open-domain Question Answering Dataset (P19-1)
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| Challenge: | Open-domain question answering aims to answer questions through text retrieval and reading comprehension . but, the success of these models relies on a massive volume of training data, which is not available in other languages . a new dataset aims at investigating cross-lingual OpenQA . |
| Approach: | They propose to use a dataset for cross-lingual OpenQA research to test models . they use XQA dataset to train models with large volumes of labeled data . |
| Outcome: | The proposed model achieves best results in almost all target languages while the performance is lower than that of English. |
Compound Probabilistic Context-Free Grammars for Grammar Induction (P19-1)
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| Challenge: | Existing approaches to grammar induction have resorted to manually-engineered features and auxiliary objectives to induce the desired structures. |
| Approach: | They propose a formalization of the grammar induction problem that models sentences as being generated by a compound probabilistic context free grammar. |
| Outcome: | Experiments on English and Chinese show that the proposed approach is more efficient than other methods. |
Semi-supervised Domain Adaptation for Dependency Parsing (P19-1)
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| Challenge: | Currently, most studies on cross-domain parsing focus on unsupervised domain adaptation . however, unsupervised approaches make limited progress due to the intrinsic difficulty of both domain adaptation and parse. |
| Approach: | They propose a semi-supervised domain adaptation problem for Chinese dependency parsing by using newly-annotated large-scale domain-aware datasets. |
| Outcome: | The proposed method is more effective than direct corpus concatenation and multi-task learning. |
Head-Driven Phrase Structure Grammar Parsing on Penn Treebank (P19-1)
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| Challenge: | Head-driven phrase structure grammars have a uniform formalism representing rich contextual syntactic and even semantic meanings. |
| Approach: | They propose to integrate constituent and dependency formal representations into head-driven phrase structure. |
| Outcome: | The proposed parser achieves state-of-the-art performance on Penn Treebank and Chinese Penn TreeBank. |
Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning (P19-1)
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| Challenge: | Empirical studies on four public NER datasets demonstrate the effectiveness of our proposed method. |
| Approach: | They propose a method to perform named entity recognition using unlabeled data and named entity dictionaries. |
| Outcome: | The proposed method can estimate task loss as if there is fully labeled data. |
Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies (P19-1)
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| Challenge: | Existing dependency parsing algorithms do not support directed acyclic graphs . a a systole-based dependency parses sentences using binary semantic relations that are not trees . |
| Approach: | They propose an iterative predicate selection algorithm for semantic dependency parsing . they train the algorithm using multi-task learning and task-specific policy gradient training . |
| Outcome: | The proposed model achieves a new state of the art on the SemEval 2015 task 18 dataset . |
GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling (P19-1)
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| Challenge: | Existing systems for sequence labeling are limited by shallow connections between consecutive hidden states and insufficient modeling of global information. |
| Approach: | They propose a global context enhanced deep transition architecture for sequence labeling . they deepen the state transition path at each position in a sentence and assign tokens with global representations . |
| Outcome: | The proposed architecture outperforms the best reported results on two standard sequence labeling tasks. |
Unsupervised Learning of PCFGs with Normalizing Flow (P19-1)
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| Challenge: | Existing induction models unable to incorporate semantics and morphology into induction . current models lack a robust model for generating morphologically rich sentences . |
| Approach: | They propose a PCFG inducer which uses context embeddings to generalize over rare, morphologically rich forms. |
| Outcome: | The proposed model produces grammars with state-of-the-art accuracy on a variety of languages. |
Variance of Average Surprisal: A Better Predictor for Quality of Grammar from Unsupervised PCFG Induction (P19-1)
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| Challenge: | In unsupervised grammar induction, data likelihood is only weakly correlated with parsing accuracy, especially at convergence after multiple runs. |
| Approach: | They propose to use VAS instead of data likelihood to find better grammars by examining linguistically-motivated constraints related to syntax. |
| Outcome: | The proposed model is better suited for word order typology classification than data likelihood. |
Cross-Domain NER using Cross-Domain Language Modeling (P19-1)
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| Challenge: | Existing methods for named entity recognition (NER) use labeled data for both source and target domains. |
| Approach: | They propose to use language modeling as a bridge between NER domains to perform cross-domain and cross-task knowledge transfer. |
| Outcome: | The proposed method extracts domain differences from cross-domain LM contrast, allowing unsupervised domain adaptation while giving state-of-the-art results among supervised domain adapters. |
Graph-based Dependency Parsing with Graph Neural Networks (P19-1)
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| Challenge: | In graph-based dependency parsers, learning representations is gaining in importance, and we use graph neural networks to learn the representations. |
| Approach: | They propose to use graph neural networks to learn dependency tree nodes and propose to add a new aggregation function to the system. |
| Outcome: | The proposed model achieves the best UAS and LAS on PTB (96.0%, 94.3%) without using external resources. |
Wide-Coverage Neural A* Parsing for Minimalist Grammars (P19-1)
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| Challenge: | a new parser for wide-coverage parsing uses a linguistically expressive yet highly constrained grammar . the expected time complexity of the parsers is cubic in the length of the sentence . |
| Approach: | They propose to use a linguistically expressive yet highly constrained grammar to parse a wide-coverage sentence using a bi-LSTM neural network supertagger. |
| Outcome: | The proposed algorithm recovers unbounded long distance dependencies and can recover unbundled long distance dependents. |
Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model (P19-1)
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| Challenge: | Existing methods to detect sarcasm focus on text, but they are insufficient for multi-modal messages. |
| Approach: | They propose a multi-modal hierarchical sarcasm detection model for tweets consisting of texts and images in Twitter. |
| Outcome: | The proposed model is able to detect sarcasm on twitter using three modalities . the proposed model can be used in customer service, opinion mining and harassment detection . |
Topic-Aware Neural Keyphrase Generation for Social Media Language (P19-1)
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| Challenge: | Existing methods to extract words from source posts to form keyphrases do not exploit latent topics. |
| Approach: | They propose a sequence-to-sequence-based neural keyphrase generation framework . it allows absent keyphrases to be created, and it allows joint modeling of latent topic representations . |
| Outcome: | The proposed model outperforms extraction and generation models without exploiting latent topics. |
#YouToo? Detection of Personal Recollections of Sexual Harassment on Social Media (P19-1)
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| Challenge: | a recent study has found that the disclosure of sexual abuse has positive psychological im- pacts. |
| Approach: | They propose to aggregate personal experiences of sexual harassment from Twitter posts to facilitate a better understanding of social media constructs and bring about social change. |
| Outcome: | The proposed model is compared with state-of-the-art models and is based on a three part Twitter-Specific Social Media Language Model. |
Multi-task Pairwise Neural Ranking for Hashtag Segmentation (P19-1)
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| Challenge: | Hashtags are used to add metadata to textual utterances, but their semantic content is difficult to infer as they often contain multiple tokens joined together. |
| Approach: | They propose to use a dataset of 12,594 hashtags to infer hashtag semantics . they propose to frame the problem as a pairwise ranking problem between candidate segmentations . |
| Outcome: | The proposed methods show 24.6% error reduction in hashtag segmentation accuracy compared to the current state-of-the-art method. |
Entity-Centric Contextual Affective Analysis (P19-1)
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| Challenge: | Existing methods for analyzing people portrayals take an unsupervised approach, or rely on domain-specific knowledge. |
| Approach: | They show how contextualized word embeddings can be used to capture affect dimensions in portrayals of people. |
| Outcome: | The proposed method can capture affect dimensions in portrayals of men and women . it is biased towards training data, which limits its usefulness to in-domain analyses . |
Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks (P19-1)
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| Challenge: | Claim verification is cumbersome and inefficient for human fact-checkers to find consistent pieces of evidence. |
| Approach: | They propose an end-to-end hierarchical attention network that learns to represent coherent evidence and their semantic relatedness with the claim. |
| Outcome: | The proposed model outperforms state-of-the-art models on three datasets . it is based on a coherence-based attention layer and entailment-based one . |
Predicting Human Activities from User-Generated Content (P19-1)
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| Challenge: | Several studies have applied computational approaches to the understanding and modeling of human behavior at scale and in real time. |
| Approach: | They propose a sentence embedding framework tailored to recognize the semantics of human activities and perform automatic clustering of these activities. |
| Outcome: | The proposed framework can make predictions based on the text of user-generated content and self-description. |
You Write like You Eat: Stylistic Variation as a Predictor of Social Stratification (P19-1)
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| Challenge: | In order to test whether and to what extent variations in writing style are influenced by socio-economic status, we used user-generated restaurant reviews on social media. |
| Approach: | They propose to use user-generated restaurant reviews to test whether and to what extent variations in writing style are influenced by socio-economic status. |
| Outcome: | The proposed model is based on user-generated restaurant reviews and user-created reviews. |
Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media (P19-1)
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| Challenge: | Identifying the political perspective shaping the way news events are discussed in the media is an important and challenging task. |
| Approach: | They propose a neural architecture for representing relational information to capture social context of news documents. |
| Outcome: | The proposed model performs better than supervised models in the supervised setting and shows that it provides a distant supervision signal. |
Fine-Grained Spoiler Detection from Large-Scale Review Corpora (P19-1)
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| Challenge: | 'Spoilers' on review websites can be a concern for consumers who want to fully experience the excitement of media consumption. |
| Approach: | They propose to use a large-scale book review dataset to generate fine-grained spoiler annotations . they then use supervised neural networks to detect spoiler sentences in review corpora . |
| Outcome: | The proposed method outperforms baselines in a large-scale book review dataset . it can detect spoiler sentences in review corpora, but only a few users use it . |
Celebrity Profiling (P19-1)
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| Challenge: | Using a corpus of 71,706 verified accounts, we construct a profile of a wide cross-section of local and global celebrities. |
| Approach: | They propose to use Twitter feeds of 71,706 verified accounts to build a corpus of celebrity profiles using Wikidata crawling. |
| Outcome: | The proposed corpus contains an average of 29,968 words per profile and up to 239 pieces of personal information. |
Dataset Creation for Ranking Constructive News Comments (P19-1)
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| Challenge: | Existing studies on comment ranking use user feedback as a quality measure . however, this type of measurement has two drawbacks: (a) user feedback does not always satisfy the service provider's needs, such as to create a fair place; and (b) user input will be biased by where comments appear in a comment thread. |
| Approach: | They propose to evaluate quality of comments on the basis of constructiveness separately from user feedback. |
| Outcome: | The proposed model improves on 100K+ Japanese comments with constructiveness scores . it shows that C-scores are not always related to positive feedback . |
Enhancing Air Quality Prediction with Social Media and Natural Language Processing (P19-1)
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| Challenge: | predicting air quality is a major concern for human health, but the changes of air quality conditions are still difficult to monitor. |
| Approach: | They propose to exploit social media and natural language processing techniques to enhance air quality prediction. |
| Outcome: | The proposed approach improves air quality prediction over baseline that does not use social media by 6.9% to 17.7% in macro-F1 scores. |
Twitter Homophily: Network Based Prediction of User’s Occupation (P19-1)
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| Challenge: | Existing approaches to predicting Twitter users' demographic attributes exploit, select, and combine various features generated from text and network to achieve the best performance. |
| Approach: | They extend existing Twitter occupational class prediction data set and exploit social network homophily to achieve competitive performance. |
| Outcome: | The proposed method achieves better performance on a dataset with a small fraction of the training data. |
Domain Adaptive Dialog Generation via Meta Learning (P19-1)
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| Challenge: | Existing methods to adapt dialog data to new domains with limited resources are expensive . a domain adaptive dialog system model learns from multiple rich-resource tasks and adapts to new tasks with minimal training samples. |
| Approach: | They propose a domain adaptive dialog generation method based on meta-learning . they train a dialog system model using multiple rich-resource single-domain dialog data . |
| Outcome: | The proposed method can learn a competitive dialog system on a new domain with minimal training examples. |
Strategies for Structuring Story Generation (P19-1)
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| Challenge: | Existing language models generate word by word, but fail to capture high-level interactions . a novel decomposition approach allows more abstract representations to be generated first . |
| Approach: | They propose models which abstract over actions and entities to create stories . they generate predicate-argument structure, then replace placeholders with context-sensitive names . |
| Outcome: | The proposed models improve diversity and coherence of events and entities in generated stories. |
Argument Generation with Retrieval, Planning, and Realization (P19-1)
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| Challenge: | a novel argument generation framework is used to generate counter-arguments . CANDELA uses a text planning decoder to retrieve arguments of different perspectives . |
| Approach: | They propose a powerful retrieval system and a novel two-step argument generation framework . they use a retrieval-based retrieval platform indexed with 12 million articles from Wikipedia . |
| Outcome: | The proposed framework yields higher BLEU, ROUGE, and METEOR scores than state-of-the-art models. |
A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation (P19-1)
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| Challenge: | Recent neural language generation systems often hallucinate contents when trained on loosely corresponding pairs of the input structure and text. |
| Approach: | They propose to integrate a language understanding module for data refinement with self-training iterations to induce strong equivalence between the input data and the paired text. |
| Outcome: | Experiments on the E2E challenge dataset show that the proposed framework reduces relative unaligned noise by 50% compared with the current state-of-the-art ensemble generator. |
Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information (P19-1)
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| Challenge: | Existing work on commenting based on textual content is focused on other modalities, such as graphics and images. |
| Approach: | They propose a task to integrate multiple modalities into automatic commenting . they construct a large-scale dataset and propose 'co-attention' model to capture dependency between textual and visual information. |
| Outcome: | The proposed model can achieve better performance than baselines. |
A Working Memory Model for Task-oriented Dialog Response Generation (P19-1)
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| Challenge: | Existing models to integrate external Knowledge Base information, one form of world knowledge, confound dialog history with KB tuples and store them into one memory. |
| Approach: | They propose a working memory model that interacts with two long-term memories to generate dialog responses. |
| Outcome: | The proposed model outperforms the state-of-the-art models on two task-oriented dialog datasets. |
Cognitive Graph for Multi-Hop Reading Comprehension at Scale (P19-1)
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| Challenge: | a new framework for multi-hop reading comprehension question answering is needed to cross the chasm of reading comprehension between machine and human. |
| Approach: | They propose a CogQA framework for multi-hop reading comprehension question answering in web-scale documents that builds a cognitive graph in an iterative process by coordinating an implicit extraction module and an explicit reasoning module. |
| Outcome: | The proposed framework outperforms the best competitor in the hotpotQA dataset in F1 . it provides explainable reasoning paths and accurate answers, while giving accurate answers . |
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs (P19-1)
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| Challenge: | Existing models to tackle multi-hop reading comprehension (RC) are focusing on a single document or paragraph, but they lack the ability to do reasoning across multiple documents. |
| Approach: | They propose a heterogeneous document-entity graph with different types of nodes and edges to solve multi-hop RC problem. |
| Outcome: | The proposed model can do reasoning over the proposed graph with nodes representation initialized with co-attention and self-attention based context encoders. |
Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension (P19-1)
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| Challenge: | Existing models for multi-hop reading comprehension only require a single-hop reasoning, meaning that the evidence needed to answer the question is scattered in a set of supporting documents. |
| Approach: | They propose an interpretable 3-module system called Explore-Propose-Assemble reader (EPAr) that explores and connects relevant information from multiple documents in order to answer a question about the context. |
| Outcome: | The proposed model approximates coarse-to-fine-grained comprehension behavior of human readers when facing multiple long documents. |
Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for Multi-Hop QA (P19-1)
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| Challenge: | In multi-hop question answering, models need to connect multiple pieces of evidence scattered in a long context to answer the question. |
| Approach: | They propose to use a control unit that dynamically attends to the question at different reasoning hops to guide the model's multi-hop reasoning. |
| Outcome: | The proposed model outperforms baseline models but is limited on adversarial test. |
Exploiting Explicit Paths for Multi-hop Reading Comprehension (P19-1)
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| Challenge: | Existing approaches to multi-hop reading comprehension do not include multiple sentences or passages. |
| Approach: | They propose a path-based reasoning approach for a multi-hop reading comprehension task . they propose to extract paths from text and compose them to encode them . |
| Outcome: | The proposed model outperforms previous models on the multi-hop Wikihop dataset and can be generalized to the OpenBookQA dataset. |
Sentence Mover’s Similarity: Automatic Evaluation for Multi-Sentence Texts (P19-1)
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| Challenge: | Existing automatic metrics for evaluating text are expensive and time-consuming. |
| Approach: | They propose automatic metrics that evaluate text in a continuous space using word and sentence embeddings. |
| Outcome: | The proposed method outperforms ROUGE on machine-generated summaries and human-authored essays on human-generated texts. |
Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains (P19-1)
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Claudia Schulz, Christian M. Meyer, Jan Kiesewetter, Michael Sailer, Elisabeth Bauer, Martin R. Fischer, Frank Fischer, Iryna Gurevych
| Challenge: | Existing deep learning methods require large amounts of training data to achieve reasonable performance. |
| Approach: | They propose to generate automatic annotation suggestions for a discourse-level sequence labelling task that requires extensive domain expertise. |
| Outcome: | The proposed model improves with newly annotated texts while introducing no biases. |
Deep Dominance - How to Properly Compare Deep Neural Models (P19-1)
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| Challenge: | Existing methods for comparing DNNs on unseen data are not suitable for this task. |
| Approach: | They propose to adapt a test for the Almost Stochastic Dominance relation between two distributions to the problem by comparing their performance on unseen data. |
| Outcome: | The proposed method meets all criteria while previously proposed methods fail to do so. |
We Need to Talk about Standard Splits (P19-1)
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| Challenge: | Existing methods to evaluate systems with a held-out test set are insufficient for system comparison. |
| Approach: | They propose to use multiple random splits to compare performance of systems . they replicate results on standard split but fail to reproduce some rankings . |
| Outcome: | The proposed method is based on multiple random splits to replicate results with a set of part-of-speech taggers. |
Aiming beyond the Obvious: Identifying Non-Obvious Cases in Semantic Similarity Datasets (P19-1)
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| Challenge: | Existing datasets for scoring text pairs in terms of semantic similarity contain instances whose resolution differs according to the degree of difficulty. |
| Approach: | They propose to use lexical overlap to distinguish obvious from non-obvious text pairs by focusing on item difficulty and ground-truth labels to characterise existing datasets. |
| Outcome: | The proposed models are based on lexical overlap and ground-truth labels and focus on cases of similarity which require more complex inference. |
Putting Evaluation in Context: Contextual Embeddings Improve Machine Translation Evaluation (P19-1)
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| Challenge: | Existing evaluation metrics are limited and can be easily portable to new languages. |
| Approach: | They propose a simple unsupervised metric and additional supervised metrics which rely on contextual word embeddings to encode the translation and reference sentences. |
| Outcome: | The proposed model outperforms existing metrics on the WMT 2017 dataset and is more accurate than existing models. |
Joint Effects of Context and User History for Predicting Online Conversation Re-entries (P19-1)
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| Challenge: | Existing methods for predicting online conversation re-entry focus on modeling engagement patterns in ongoing conversations or ignoring the rich information in users' previous chatting history. |
| Approach: | They propose a neural framework with three main layers to model the conversation context and user history and their interactions with Twitter and Reddit to predict whether a user will return to a conversation they once participated in. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on two large-scale Twitter and Reddit conversations, and achieves an F1 score of 61.1 on Twitter conversations. |
CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech (P19-1)
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| Challenge: | Davidson et al., 2017): social media platforms and governmental organizations have taken steps to tackle hate speech . Davidson and Norton, 2017: a dataset of hate-speech/counter-narrative pairs is created . authors: identifying hate speech is challenging for the broadness and nuances in cultures and languages . |
| Approach: | They propose to build a large-scale, multilingual, expert-based dataset of hate-speech/counter-narrative pairs . they provide additional annotations about expert demographics, hate and response type . |
| Outcome: | The proposed dataset provides an analysis of hate-speech/counter-narrative pairs in three languages. |
Categorizing and Inferring the Relationship between the Text and Image of Twitter Posts (P19-1)
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| Challenge: | Social media posts often contain images to provide content, provide context, or express feelings. |
| Approach: | They build and release a dataset of image tweets annotated with four different classes which express whether the text or the image provides additional information to the other modality. |
| Outcome: | The proposed method can be used in several downstream applications including pre-training image tagging models and collecting distantly supervised data for image captioning. |
Who Sides with Whom? Towards Computational Construction of Discourse Networks for Political Debates (P19-1)
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| Challenge: | a vision of computational construction of discourse networks from newspaper reports is essential for understanding democratic political decision making. |
| Approach: | They propose to use a requirements analysis and an annotated pilot corpus of migration claims to build a computationally-based model of political debates from newspaper reports. |
| Outcome: | The proposed framework could be scaled up to a large scale and be useful for political scientists. |
Analyzing Linguistic Differences between Owner and Staff Attributed Tweets (P19-1)
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| Challenge: | Existing studies on social media assume that all tweets are authored by the same person . this paper examines the linguistic differences between posts signed by the account owner and staff attributed to the account's owner . |
| Approach: | They analyze linguistic differences between tweets signed by the account owner and staff attributed to their staff. |
| Outcome: | The proposed model predicts owner and staff attributed tweets with good accuracy even without training data from the account. |
Exploring Author Context for Detecting Intended vs Perceived Sarcasm (P19-1)
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| Challenge: | Existing studies on textual sarcasm detection use manual labelling and tag-based distant supervision to detect sarcasm. |
| Approach: | They define author context as the embedded representation of their historical tweets and suggest neural models that extract these representations. |
| Outcome: | The proposed models achieve state-of-the-art on two datasets labelled manually and via tag-based distant supervision indicating a difference between intended and perceived sarcasm . |
Open Domain Event Extraction Using Neural Latent Variable Models (P19-1)
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| Challenge: | Existing work on extracting events from news documents focuses on a set of pre-specified event types. |
| Approach: | They propose a latent variable neural model which is scalable to large corpus. |
| Outcome: | The proposed model performs better than the state-of-the-art method for event schema induction. |
Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification (P19-1)
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| Challenge: | Existing methods for few-shot relation classification use supervised training, but lack of large-scale manually labeled data. |
| Approach: | They propose a multi-level matching and aggregation network (MLMAN) for few-shot relation classification. |
| Outcome: | The proposed model achieves state-of-the-art performance on the FewRel dataset. |
Quantifying Similarity between Relations with Fact Distribution (P19-1)
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| Challenge: | a conceptually simple and effective method to quantify the similarity between relations is presented . identifying relations is a crucial problem for several information extraction tasks. |
| Approach: | They propose a method to quantify the similarity between relations in knowledge bases . they use a neural network to parameterize conditional probability distributions over entity pairs . |
| Outcome: | The proposed method significantly correlates with human judgments, the authors show . it could be incorporated into negative sampling and softmax classification to alleviate these mistakes. |
Matching the Blanks: Distributional Similarity for Relation Learning (P19-1)
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| Challenge: | Efforts to build general purpose relation extractors that can model arbitrary relations are limited in their ability to generalize. |
| Approach: | They propose to build task-agnostic relation representations solely from entity-linked text to extend Harris’ distributional hypothesis to relations. |
| Outcome: | The proposed representations outperform previous methods on SemEval 2010 Task 8, KBP37, and TACRED even without using any of the task’s training data. |
Fine-Grained Temporal Relation Extraction (P19-1)
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| Challenge: | Existing methods for temporal relations and event durations are insufficient for determining the fine-grained temporal structure of complex events. |
| Approach: | They propose a semantic framework for temporal relations and event durations that maps pairs of events to real-valued scales. |
| Outcome: | The proposed framework can predict fine-grained temporal relations and event durations . it can be applied to the entire English Web Treebank dataset . |
FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithms (P19-1)
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| Challenge: | evaluating models is a non-trivial task and requires extensive data and data splits to produce reliable comparisons. |
| Approach: | They propose a model selection approach that reduces the computational resources required to compare models based on single choices of random seeds. |
| Outcome: | The proposed model selection approach reduces the computational resources required to identify state-of-the-art models from large datasets. |
Is Attention Interpretable? (P19-1)
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| Challenge: | Attention mechanisms have recently boosted performance on a range of NLP tasks. |
| Approach: | They propose to manipulate attention weights in text classification models and analyze the resulting differences in their predictions. |
| Outcome: | The proposed approach improves models' predictions by using gradient-based rankings of attention weights. |
Correlating Neural and Symbolic Representations of Language (P19-1)
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| Challenge: | a popular technique for analyzing neural representations involves predicting information of interest from the activation patterns. |
| Approach: | They propose to use Representational Similarity Analysis and Tree Kernels to quantify how strongly activation patterns correspond to symbolic representations. |
| Outcome: | The proposed methods show that they exhibit the expected pattern of results on a synthetic language. |
Interpretable Neural Predictions with Differentiable Binary Variables (P19-1)
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| Challenge: | Neural networks are bringing incredible performance gains on text classification tasks, but they also require interpretability. |
| Approach: | They propose a latent model that selects a rationale and a classifier that learns from the words in the rationale alone. |
| Outcome: | The proposed model can predict expected value of penalties without REINFORCE and can be directly optimised towards a pre-specified text selection rate. |
Transformer-XL: Attentive Language Models beyond a Fixed-Length Context (P19-1)
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| Challenge: | Term memory networks (RNNs) are difficult to optimize due to gradient vanishing and explosion. |
| Approach: | They propose a neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. |
| Outcome: | The proposed method improves state-of-the-art performance on short and long sequences and generates coherent, novel text articles with thousands of tokens. |
Domain Adaptation of Neural Machine Translation by Lexicon Induction (P19-1)
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| Challenge: | Neural machine translation (NMT) is sensitive to domain shift, resulting in failure for sentences with large numbers of unknown words and lack of supervision for domain-specific words. |
| Approach: | They propose an unsupervised method which fine-tunes a pre-trained out-of-domain NMT model using a pseudo-in-domain corpus. |
| Outcome: | The proposed method improves in five domains without using in-domain parallel sentences and up to 2 BLEU over strong back-translation baselines. |
Reference Network for Neural Machine Translation (P19-1)
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| Challenge: | Neural Machine Translation (NMT) generates translations in isolation, resulting in translation inconsistency and ambiguity. |
| Approach: | They propose to incorporate referring process into translation decoding of NMT by using local coordinates coding to obtain global context vectors containing monolingual and bilingual contextual information. |
| Outcome: | The proposed model improves translation quality with lightweight computation cost on Chinese-English and English-German translation tasks. |
Retrieving Sequential Information for Non-Autoregressive Neural Machine Translation (P19-1)
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| Challenge: | Experimental results show that the Reinforce-NAT system surpasses the baseline NAT system by a significant margin on BLEU without decelerating the decoding speed. |
| Approach: | They propose a sequence-level training method and a Transformer decoder to fuse the target sequential information into the top layer of the decoded Transformer. |
| Outcome: | The proposed model surpasses the baseline NAT system on BLEU without decelerating the decoding speed and achieves comparable translation performance to the autoregressive Transformer model with considerable speedup. |
STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework (P19-1)
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Mingbo Ma, Liang Huang, Hao Xiong, Renjie Zheng, Kaibo Liu, Baigong Zheng, Chuanqiang Zhang, Zhongjun He, Hairong Liu, Xing Li, Hua Wu, Haifeng Wang
| Challenge: | Simultaneous translation is notoriously dif- ficult due to word-order differences. |
| Approach: | They propose a prefix-to-prefix framework that implicitly learns to anticipate in a single translation model. |
| Outcome: | The proposed framework achieves low latency and reasonable qual- ity on 4 directions. |
Look Harder: A Neural Machine Translation Model with Hard Attention (P19-1)
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| Challenge: | Soft-attention based Neural Machine Translation models attend all the words in the source sequence for each target token, which makes them ineffective for long sequence translation. |
| Approach: | They propose a hard-attention based NMT model which selects a subset of source tokens for each target token to effectively handle long sequence translation. |
| Outcome: | The proposed model performs better on long sequences and achieves significant improvement on English-German and English-French translation tasks compared to soft-attention based models. |
Robust Neural Machine Translation with Joint Textual and Phonetic Embedding (P19-1)
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| Challenge: | Neural machine translation models are sensitive to noises in input sentences . one special kind of noise is the homophone noise, where words are replaced by other words with similar pronunciations. |
| Approach: | They propose to embed phonetic and textual information into neural machine translation datasets to improve robustness to homophone noises. |
| Outcome: | The proposed method improves the robustness of neural machine translation to homophone noises on clean test sets. |
A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning (P19-1)
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| Challenge: | Existing APE systems generate artificial triplets of source sentences, machine translation outputs and human post-edits. |
| Approach: | They propose to use human post-edits to refine black-box machine translation (MT) models by fine-tuning pre-trained BERT models on both encoder and decoder of an APE system. |
| Outcome: | The proposed method improves on a dataset of 23K sentences on x86 GPUs. |
Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation (P19-1)
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| Challenge: | Using a dictionary, given a rough, target language natives can uncover the latent, fully-fluent rendering of the translation. |
| Approach: | They propose a method that breaks translation into two steps by generating a dictionary and then ‘translating’ the resulting pseudo-translation into a fully fluent translation. |
| Outcome: | The proposed method 'gets better translation results on high-resource languages than previously published unsupervised MT studies' |
Training Neural Machine Translation to Apply Terminology Constraints (P19-1)
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| Challenge: | Existing methods to integrate domain terminology into neural machine translation (NMT) are brittle when tested in real-world situations. |
| Approach: | They propose a method to inject custom terminology into neural machine translation at run time by using the target side of terminology entries whose source side match the input as decoding-time constraints. |
| Outcome: | The proposed method is faster than state-of-the-art decoding and more efficient than constraint-free decoding. |
Leveraging Local and Global Patterns for Self-Attention Networks (P19-1)
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| Challenge: | Existing approaches to integrate local and global information into self-attention networks have been criticized for overlooking neighboring information. |
| Approach: | They propose a hybrid attention mechanism to leverage local and global information . they use a gating scalar to integrate both sources of information based on local contexts . |
| Outcome: | The proposed approach improves on translation tasks and shows that the two types of contexts are complementary. |
Sentence-Level Agreement for Neural Machine Translation (P19-1)
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| Challenge: | Empirical results show that a sentence-level agreement module can significantly improve the performance of neural machine translation (NMT) |
| Approach: | They propose a sentence-level agreement module to minimize the difference between the representation of source and target sentences. |
| Outcome: | Empirical results show the proposed agreement module significantly improves translation performance. |
Multilingual Unsupervised NMT using Shared Encoder and Language-Specific Decoders (P19-1)
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| Challenge: | Existing approaches to train multiple languages with a shared encoder and multiple decoders are based on denoising autoencoding of each language and back-translating between English and multiple non-English languages. |
| Approach: | They propose a multilingual unsupervised NMT scheme which trains multiple languages with a shared encoder and multiple decoders. |
| Outcome: | The proposed model performs better than the separately trained bilingual models on monolingual corpora and improves by 1.48 BLEU points on WMT test sets. |
Lattice-Based Transformer Encoder for Neural Machine Translation (P19-1)
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| Challenge: | Neural machine translation (NMT) takes deterministic sequences for source representations. However, word-level or subword-level segmentation has multiple choices to split a source sequence with different word segmentors or different subword vocabulary sizes. |
| Approach: | They propose lattice-based encoders to explore effective word or subword representations in an automatic way during training. |
| Outcome: | The proposed encoders can explore effective word or subword representation in an automatic way during training. |
Multi-Source Cross-Lingual Model Transfer: Learning What to Share (P19-1)
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| Challenge: | Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language . however, many languages lack the labeled training data necessary for training deep neural nets for varying NLP tasks. |
| Approach: | They propose a cross-lingual transfer learning method that leverages annotated data from other languages to build NLP models for a target language. |
| Outcome: | The proposed model achieves significant performance gains over prior art over multiple text classification and sequence tagging tasks including a large-scale industry dataset. |
Unsupervised Multilingual Word Embedding with Limited Resources using Neural Language Models (P19-1)
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| Challenge: | Existing methods that map word embeddings into a common space without any parallel data or pre-training have been proposed that are limited in resources and perform poorly under resource-poor conditions. |
| Approach: | They propose a model that maps monolingual word embeddings into a common space without any parallel data and generates multilingual embeddables without any pre-training. |
| Outcome: | The proposed model outperforms existing methods on word alignment tasks on low-resource conditions and with limited resources. |
Choosing Transfer Languages for Cross-Lingual Learning (P19-1)
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Yu-Hsiang Lin, Chian-Yu Chen, Jean Lee, Zirui Li, Yuyan Zhang, Mengzhou Xia, Shruti Rijhwani, Junxian He, Zhisong Zhang, Xuezhe Ma, Antonios Anastasopoulos, Patrick Littell, Graham Neubig
| Challenge: | Cross-lingual transfer is a useful tool for improving performance of natural language processing (NLP) on low-resource languages. |
| Approach: | They propose to use cross-lingual transfer to improve accuracy of low-resource languages . they build models that consider features to perform prediction on such languages based on ranking problem . |
| Outcome: | The proposed model predicts good transfer languages much better than baselines considering single features in isolation. |
CogNet: A Large-Scale Cognate Database (P19-1)
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| Challenge: | Existing cognate databases have limited practical applications for research, despite their wide coverage and limited use in lexical tasks. |
| Approach: | They introduce a new large-scale lexical database that provides cognates across languages. |
| Outcome: | The proposed database contains 3.1 million cognate pairs across 338 languages and has an accuracy of 94%. |
Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B (P19-1)
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| Challenge: | Existing methods for decipherment of lost languages are limited by limited data and scarce quantities of ancient text. |
| Approach: | They propose a neural approach for automatic decipherment of lost languages . they use an expressive sequence-to-sequence model to capture character-level correspondences between cognates . |
| Outcome: | The proposed approach improves on the decipherment of Ugaritic and Linear B in ancient Greek . the proposed approach is highly customized for a given language pair and does not generalize to other lost languages. |
Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network (P19-1)
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| Challenge: | Existing approaches to cross-lingual knowledge graph (KG) alignment rely on entity embeddings derived from monolingual KG structural information. |
| Approach: | They propose a topic entity graph to represent entities with contextual information in KGs. |
| Outcome: | The proposed model outperforms state-of-the-art methods by a large margin. |
Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention (P19-1)
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| Challenge: | Abstractive Sentence Summarization (ASSUM) is a monolingual task that focuses on grasping the core idea of the source sentence and presenting it as the summary. |
| Approach: | They propose to use monolingual ASSUM to train a cross-lingual ASL system . they propose to train the system on summary word generation and attention . |
| Outcome: | Experiments show that the proposed method improves on the monolingual ASSUM task. |
Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations (P19-1)
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Rui Zhang, Caitlin Westerfield, Sungrok Shim, Garrett Bingham, Alexander Fabbri, William Hu, Neha Verma, Dragomir Radev
| Challenge: | Experimental results show that our model outperforms competitive translation-based baselines on cross-lingual relevance ranking tasks. |
| Approach: | They propose to match queries and documents in both source and target languages with deep bilingual query-document representations. |
| Outcome: | The proposed model outperforms translation-based baselines on English-Swahili, English-Tagalog, and English-Somali cross-lingual retrieval tasks. |
Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization (P19-1)
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| Challenge: | Cross-lingual word embeddings (CLWE) are used to perform multilingual natural language processing tasks. |
| Approach: | They propose a method that transforms monolingual embeddings to make orthogonal alignment easier by simultaneously enforcing that (1) individual word vectors are unit length, and (2) each language’s average vector is zero. |
| Outcome: | The proposed method improves translation accuracy of three CLWE methods, with the largest improvement observed on English-Japanese (2% to 44% test accuracy). |
MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction (P19-1)
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| Challenge: | Existing work shows that morphological variation is an intractable challenge for the unsupervised bilingual lexicon induction task. |
| Approach: | They propose a morphology-aware alignment model to alleviate the adverse effect of morphological variation by introducing grammatical information learned by the pre-trained denoising language model. |
| Outcome: | The proposed model outperforms state-of-the-art unsupervised systems and achieves competitive performance compared to supervised methods. |
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings (P19-1)
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| Challenge: | Traditional parallel corpus mining methods focus on the textual content instead of the size and quality of training data. |
| Approach: | They propose a method for machine translation based on multilingual sentence embeddings. |
| Outcome: | The proposed method outperforms the best published methods on the BUCC mining task and the UN reconstruction task by more than 10 F1 and 30 precision points. |
JW300: A Wide-Coverage Parallel Corpus for Low-Resource Languages (P19-1)
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| Challenge: | a shortage of parallel data in low-resource languages creates a bottleneck for cross-lingual transfer . a massive collection of parallel texts for over 300 diverse languages is our main contribution . |
| Approach: | They propose a parallel corpus of over 300 languages with 100 thousand parallel sentences per language pair on average. |
| Outcome: | The proposed dataset can be used to build cross-lingual word embeddings and multi-source part-of-speech projections. |
Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections (P19-1)
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| Challenge: | Current systems for syntactic analysis tasks rely heavily on large scale annotated data. |
| Approach: | They propose to learn a generative model with a structured prior that uses labeled source and unlabeled target data jointly. |
| Outcome: | The proposed model improves on part-of-speech tagging and dependency parsing tasks on English as the only source corpus and on a wide range of target languages. |
Unsupervised Joint Training of Bilingual Word Embeddings (P19-1)
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| Challenge: | Existing methods for unsupervised bilingual word embeddings are limited by the dissimilarity between the word embedded spaces. |
| Approach: | They propose a method that trains unsupervised bilingual word embeddings jointly on parallel data generated through unsupervised machine translation. |
| Outcome: | The proposed method outperforms unsupervised mapped bilingual word embeddings in cross-lingual NLP tasks. |
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings (P19-1)
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| Challenge: | Using hyperbolic embeddings, we can infer concept hierarchies from distributional contexts while also being able to predict missing “is-a”-relationships and correct wrong extractions. |
| Approach: | They propose a method combining hyperbolic embeddings and Hearst patterns to set appropriate constraints for inferring “is-a” relationships from large text corpora and improve taxonomic consistency. |
| Outcome: | The proposed method achieves state-of-the-art performance on a variety of hypernymy benchmarks. |
Is Word Segmentation Necessary for Deep Learning of Chinese Representations? (P19-1)
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| Challenge: | Using word-based models, we compare word-oriented models with char-based ones . word-driven models are more vulnerable to data sparsity and the presence of out-of-vocabulary words . |
| Approach: | They benchmark word-based models with char-based model which does not involve word segmentation in four NLP benchmark tasks. |
| Outcome: | The proposed model outperforms char-based models in four NLP benchmark tasks. |
Towards Understanding Linear Word Analogies (P19-1)
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| Challenge: | Existing theories of word embeddings make strong assumptions about the embeddable space or word distribution. |
| Approach: | They propose a formal explanation of word analogies by adding arithmetic operators to non-linear embedding models such as skip-gram with negative sampling. |
| Outcome: | The proposed model downweights the more frequent word, as weighting schemes do ad hoc. |
On the Compositionality Prediction of Noun Phrases using Poincaré Embeddings (P19-1)
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| Challenge: | idiomatic phrases have a non-compositional meaning, meanings of which can be derived from constituents and their grammatical relations. |
| Approach: | They propose to combine hierarchical and distributional information to blend hierarchic and distribution-based hierarchies to detect compositionality for noun phrases. |
| Outcome: | The proposed technique achieves significant improvements over state-of-the-art models based on distributional information and a weighted average of the distributional similarity and p-like function. |
Robust Representation Learning of Biomedical Names (P19-1)
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| Challenge: | Biomedical concepts are often mentioned in medical documents under different name variations. |
| Approach: | They propose a framework for learning robust representations of biomedical names and terms . they encode contextual meaning, conceptual meaning, and similarity between synonyms . |
| Outcome: | The proposed framework outperforms baselines on retrieval, similarity and relatedness benchmarks. |
Relational Word Embeddings (P19-1)
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| Challenge: | Existing approaches to learn word embeddings rely on external knowledge bases . however, they are limited by the amount of available relational knowledge . |
| Approach: | They propose to encode relational knowledge in a separate word embedding . this is complementary to a standard word embedded from co-occurrence statistics . |
| Outcome: | The proposed word embedding is complementary to a standard word embed. |
Unraveling Antonym’s Word Vectors through a Siamese-like Network (P19-1)
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| Challenge: | Discriminating antonyms and synonyms is difficult because they contain similar distributional information . word vectors are lexical relations that are crucial in language semantics . hand crafted lexicals such as Word-Net have been built and maintained to be used in NLP . |
| Approach: | They propose an approach to unravel antonymy and synonymy from word vectors using a siamese network inspired approach. |
| Outcome: | The proposed approach outperforms distributional and pattern-based approaches in terms of antitransitivity and accuracy. |
Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks (P19-1)
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Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar
| Challenge: | Existing word embedding methods utilize sequential context of a word to learn its embeddment, but such methods result in an explosion of the vocabulary size. |
| Approach: | They propose a flexible Graph Convolution based method for learning word embeddings that utilizes the dependency context of a word without increasing the vocabulary size. |
| Outcome: | The proposed model outperforms existing methods on intrinsic and extrinsic tasks and provides an advantage when used with ELMo. |
Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors (P19-1)
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| Challenge: | Word embedding models typically learn two types of vectors: target word vectors and context word vector. |
| Approach: | They propose to explicitly impose a cluster structure on context word vectors to improve word embedding models. |
| Outcome: | The proposed model improves word embedding models qualitatively by imposing a cluster structure on the set of context word vectors. |
Delta Embedding Learning (P19-1)
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| Challenge: | Unsupervised word embeddings have limitations to the semantics of words and inadequate fine-tuning of embedded word can lead to suboptimal performance. |
| Approach: | They propose a method that optimizes word embeddings by regularizing them incrementally to ensure they are tuned in an incremental way. |
| Outcome: | The proposed method improves performance on various NLP tasks and shows that it absorbs semantic information without "forging" |
Annotation and Automatic Classification of Aspectual Categories (P19-1)
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| Challenge: | Annotated resource for aspectual classification of German verb tokens in context. |
| Approach: | They present a resource for aspectual classification of German verb tokens in their clausal context. |
| Outcome: | The proposed resource is compared with previous work on German verb tokens using aspectual features compatible with the plurality of aspectual classifications. |
Putting Words in Context: LSTM Language Models and Lexical Ambiguity (P19-1)
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| Challenge: | In language, a word can contribute a very different meaning depending on the context . lexical ambiguity involves both morphosyntactic and semantic aspects . |
| Approach: | They propose a method to probe hidden representations for lexical and contextual information about words. |
| Outcome: | The proposed method shows that both types of information are represented to a large extent, but there is room for improvement for contextual information. |
Making Fast Graph-based Algorithms with Graph Metric Embeddings (P19-1)
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| Challenge: | Graph measures, such as node distances, are inefficient to compute. |
| Approach: | They propose a way to learn graph embeddings by using vector operations instead of a graph structure. |
| Outcome: | The proposed method outperforms other graph embeddings on word similarity and word sense disambiguation tasks. |
Embedding Imputation with Grounded Language Information (P19-1)
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| Challenge: | Existing approaches to embedding imputation use vector space properties or subword information to learn representations for rare or unseen words. |
| Approach: | They propose an online method to construct a knowledge graph from grounded information and an algorithm to map from the resulting graph to the space of the pre-trained embeddings. |
| Outcome: | The proposed method improves on a card-660 task by 11% and 17.8% respectively using GloVe embeddings. |
The Effectiveness of Simple Hybrid Systems for Hypernym Discovery (P19-1)
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| Challenge: | Recent work utilizing a mix of pattern-based and distributional approaches have yielded state-of-the-art results on two domain-specific English hypernym discovery tasks. |
| Approach: | They evaluate the contribution of pattern-based and distributional approaches to hybrid modeling by evaluating baseline models from each paradigm. |
| Outcome: | The proposed approach outperforms all non-hybrid approaches on two domain-specific English hypernym discovery tasks and outperformed other approaches. |
BERT-based Lexical Substitution (P19-1)
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| Challenge: | Existing approaches to lexical substitution tend to overlook good substitute candidates that are not the synonyms of the target words in the lexicals and fail to take into account the substitution’s influence on the global context of the sentence. |
| Approach: | They propose an end-to-end BERT-based lexical substitution approach which proposes and validates substitute candidates without using annotated data or manually curated resources. |
| Outcome: | The proposed approach performs well in proposing and ranking substitute candidates, achieving the state-of-the-art results in both LS07 and LS14 benchmarks. |
Exploring Numeracy in Word Embeddings (P19-1)
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| Challenge: | Existing word embeddings are inadequate at capturing numerical properties of numbers. |
| Approach: | They propose to use word embeddings to capture numerical properties of numbers . they hope to develop methods which better capture numeric properties . |
| Outcome: | The proposed models lack the ability to capture numeric properties of numbers, the authors show . their findings provide a starting point for the development of better models . |
HighRES: Highlight-based Reference-less Evaluation of Summarization (P19-1)
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| Challenge: | Existing methods for summarizing documents are inconsistent due to the difficulty of manual evaluation. |
| Approach: | They propose a method where summaries are evaluated by multiple annotators against the source document via manually highlighted salient content. |
| Outcome: | The proposed method improves inter-annotator agreement while highlighting differences among systems. |
EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing (P19-1)
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| Challenge: | Current sentence simplification systems are variants of sequence-to-sequence models adopted from machine translation. |
| Approach: | They propose a sentence simplification model that learns explicit edit operations via a neural programmer-interpreter approach. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark text simplification corpora in terms of SARI (+0.95 WikiLarge, +1.89 WikiSmall, -1.41 Newsela) |
Decomposable Neural Paraphrase Generation (P19-1)
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| Challenge: | Existing models learn to generate paraphrases by mapping a sequence to another, with each word processed and generated in a uniform way. |
| Approach: | They propose a Transformer-based model that can learn and generate paraphrases at different levels of granularity in a disentangled way. |
| Outcome: | The proposed model achieves competitive in-domain performance compared to state-of-the-art models and significantly better performance when adapting to a new domain. |
Transforming Complex Sentences into a Semantic Hierarchy (P19-1)
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| Challenge: | Existing approaches for recursively splitting and rephrasing complex English sentences into a semantic hierarchy of simplified sentences are lacking. |
| Approach: | They propose a method for recursively splitting and rephrasing complex English sentences into a semantic hierarchy of simplified sentences. |
| Outcome: | The proposed approach outperforms state-of-the-art approaches in MT and information extraction tasks. |
Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference (P19-1)
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| Challenge: | lexical overlap heuristics are effective for frequent example types but break down in more challenging cases. |
| Approach: | They propose to use a set of examples to test whether a sentence entails another . they propose to adopt three fallible syntactic heuristics for statistical NLI models . |
| Outcome: | The proposed model performs poorly on the HANS dataset, suggesting they have adopted these heuristics. |
Zero-Shot Entity Linking by Reading Entity Descriptions (P19-1)
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| Challenge: | Existing approaches to link entities to unseen entities require in-domain labeled data. |
| Approach: | They propose a zero-shot entity linking task where mentions must be linked to unseen entities without in-domain labeled data. |
| Outcome: | The proposed task can generalize to unseen entities without metadata or alias tables . the proposed system improves over baselines, including BERT, on a new dataset . |
Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition (P19-1)
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| Challenge: | Named entity recognition (NER) is an important step in most natural language processing (NLP) applications. |
| Approach: | They propose a dual-adversarial neural transfer method for addressing low-resource Named Entity Recognition (NER) they propose 'Generalized Resource-Adversarial Discriminator' and 'accidental training' |
| Outcome: | The proposed method improves on low-resource Named Entity Recognition (NER) with two variants, i.e., DATNet-F and DATNET-P, and adversarial training is adopted to boost model generalization. |
Scalable Syntax-Aware Language Models Using Knowledge Distillation (P19-1)
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| Challenge: | Prior work has shown that syntactic neural language models learn from small amounts of training data more effectively than sequential models. |
| Approach: | They propose a knowledge distillation technique that transfers knowledge from a syntactic language model trained on a small corpus to an LSTM language model and enables it to develop a more structurally sensitive representation of the larger training data. |
| Outcome: | The proposed method improves on baseline syntactic evaluations on LSTMs with a higher level of accuracy than previous methods. |
An Imitation Learning Approach to Unsupervised Parsing (P19-1)
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| Challenge: | Unsupervised parsing is a form of reinforcement learning that improves syntactic structures but lacks interpretability due to its lack of ad hoc heuristics. |
| Approach: | They propose an unsupervised approach that transfers syntactic knowledge to a Tree-LSTM model with discrete parsing actions. |
| Outcome: | The proposed model outperforms existing models on the All Natural Language Inference dataset and achieves a new state of the art in terms of parsing F-score. |
Women’s Syntactic Resilience and Men’s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing (P19-1)
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| Challenge: | linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency parsing have not adapted to account for these differences. |
| Approach: | They annotate the Wall Street Journal part of the Penn Treebank with the gender information of the articles’ authors and build taggers and parsers trained on this data. |
| Outcome: | The proposed model can account for gendered differences in syntactic tasks and highlight future venues for developing more accurate taggers and parsers. |
Multilingual Constituency Parsing with Self-Attention and Pre-Training (P19-1)
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| Challenge: | a range of pre-training conditions can be used for constituency parsing, but large model sizes make it expensive to train separate models for each language. |
| Approach: | They compare the benefits of no pre-training, fastText, ELMo, and BERT for English . they also find that pre- training is beneficial across all 11 languages tested . |
| Outcome: | The proposed model outperforms fastText, ELMo, and BERT for English . but large model sizes make it expensive to train separate models for each language . |
A Multilingual BPE Embedding Space for Universal Sentiment Lexicon Induction (P19-1)
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| Challenge: | Existing methods for sentiment lexicon induction are limited to low-resource languages. |
| Approach: | They propose a method for sentiment lexicon induction that is applicable to the entire range of typological diversity of the world's languages. |
| Outcome: | The proposed method is applicable to the entire range of typological diversity of the world's languages. |
Tree Communication Models for Sentiment Analysis (P19-1)
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| Challenge: | Existing methods for sentiment classification over hierarchical phrases capture only bottom-up dependencies between constituents. |
| Approach: | They propose a tree-based sentiment analysis model using graph convolutional neural network and graph recurrent neural network which allows rich information exchange between phrases constituent tree. |
| Outcome: | The proposed model outperforms existing tree-LSTMs in accuracy and efficiency, providing more consistent predictions on phrase-level sentiments. |
Improved Sentiment Detection via Label Transfer from Monolingual to Synthetic Code-Switched Text (P19-1)
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| Challenge: | Existing sentiment detection methods are trained on sentiment-labeled monolingual text. |
| Approach: | They propose a method for synthesizing labeled code-switched text from monolingual text. |
| Outcome: | The proposed method improves sentiment labeling accuracy for three languages. |
Exploring Sequence-to-Sequence Learning in Aspect Term Extraction (P19-1)
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| Challenge: | Aspect term extraction (ATE) aims at identifying all aspect terms in a sentence . sequence labeling based methods cannot make full use of overall meaning of sentence if they have dependencies between labels. |
| Approach: | They propose to formalize ATE as a sequence-to-sequence (Seq2Seque) learning task . they propose gated unit networks and position-aware attention mechanism to make it suit to ATE . |
| Outcome: | The proposed learning task is effective when labels correspond to words one by one . the proposed learning system is gated unit networks and position-aware attention mechanism . |
Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network (P19-1)
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| Challenge: | Existing studies on aspect sentiment classification focus on non-interactive reviews . a new task aims to predict sentiment polarities for specific aspects from interactive reviews based on annotated corpus . |
| Approach: | They propose a task to predict aspects from interactive QA style reviews using an annotated corpus. |
| Outcome: | The proposed approach is compared with state-of-the-art methods against a high-quality corpus of data. |
ELI5: Long Form Question Answering (P19-1)
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| Challenge: | Existing question answering datasets provide extractive or short answers, but less attention has been paid to open-ended questions that require explanations. |
| Approach: | They present a large-scale corpus for long form question answering . they use a Reddit forum to provide elaborate answers to open-ended questions . |
| Outcome: | The proposed model outperforms Seq2Seq, language modeling, and other models in human evaluations. |
Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension (P19-1)
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| Challenge: | In a decade, question answering (QA) has been one of the most promising achievements in the field of natural language processing (NLP). |
| Approach: | They propose a new module f-GCN based on graph convolutional networks (GCN) to extract knowledge features from multi-modal contexts in complex input data. |
| Outcome: | The proposed model outperforms state-of-the-art methods on the textbook question answering task and on the visual features of the dataset. |
Generating Question Relevant Captions to Aid Visual Question Answering (P19-1)
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| Challenge: | Visual question answering and image captioning require a shared body of general knowledge connecting language and vision. |
| Approach: | They propose a method that exploits a shared body of general knowledge connecting language and vision by jointly generating captions. |
| Outcome: | The proposed approach obtains state-of-the-art performance on the VQA v2 challenge . it uses human annotated captions to generate question-relevant captions . |
Multi-grained Attention with Object-level Grounding for Visual Question Answering (P19-1)
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| Challenge: | Current approaches to visual question answering train attention models from coarse-grained associations between sentences and images, which fail on small objects or uncommon concepts. |
| Approach: | They propose a multi-grained attention method that learns explicit word-object correspondence by word-level attention complementary to the sentence-image association. |
| Outcome: | The proposed method achieves competitive performance with state-of-the-art models on the VQA benchmark. |
Psycholinguistics Meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering (P19-1)
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| Challenge: | Existing methods to overcome catastrophic forgetting in visual question answering models are inadequate, but have received little attention within natural language processing. |
| Approach: | They devise a set of linguistically-informed visual question answering tasks motivated by psycholinguistics and investigate impact of task difficulty on continual learning. |
| Outcome: | The proposed models differ in the types of questions they ask and show that task difficulty and order matter. |
Improving Visual Question Answering by Referring to Generated Paragraph Captions (P19-1)
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| Challenge: | Empirical results show that paragraph captions help answer more visual questions . |
| Approach: | They propose a visual and textual question answering model which uses paragraph captions as input . they use cross-attention to extract related information, then consensus to fuse the inputs . |
| Outcome: | Empirical results show that paragraph captions help answer more visual questions . the proposed model significantly improves the baseline model . |
Shared-Private Bilingual Word Embeddings for Neural Machine Translation (P19-1)
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| Challenge: | Word embedding is central to neural machine translation, but indirectly interfaces with other layers, making them comparatively isolated. |
| Approach: | They propose a shared-private bilingual word embedding which gives a closer relationship between the source and target embedders and reduces the number of model parameters. |
| Outcome: | The proposed model improves on 5 language pairs belonging to 6 different language families and written in 5 different alphabets and significantly reduces model parameters. |
Literary Event Detection (P19-1)
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| Challenge: | a new dataset of literary events is presented to examine the nature of narratives . literature presents a number of challenges for existing systems, including complex narration . |
| Approach: | They propose a dataset of literary events that are depicted as taking place within the imagined space of a novel. |
| Outcome: | The proposed model achieves an F1 score of 73.9 for prestige and popularity . the best performing model achieve a score of 79.9 for prestige compared to the previous model . |
Assessing the Ability of Self-Attention Networks to Learn Word Order (P19-1)
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| Challenge: | Existing studies have attributed SAN to being weak at learning positional information for sequence modeling due to lack of recurrence structure. |
| Approach: | They propose a word reordering detection task to quantify how well word order information is learned by SAN and RNN. |
| Outcome: | The proposed task quantifies how well word order information learned by SAN and RNN is learned. |
Energy and Policy Considerations for Deep Learning in NLP (P19-1)
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| Challenge: | Recent advances in hardware and methodology for training neural networks have enabled significant accuracy improvements across many NLP tasks. |
| Approach: | They quantify the approximate financial and environmental costs of training neural network models . they propose actionable recommendations to reduce costs and improve equity in NLP research . |
| Outcome: | The proposed recommendations address the cost and environmental costs of training neural networks for NLP. |
What Does BERT Learn about the Structure of Language? (P19-1)
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| Challenge: | BERT is a language representation model that has performed well in diverse language understanding benchmarks. |
| Approach: | They perform experiments to unpack the elements of English language structure learned by BERT. |
| Outcome: | The proposed model outperforms state-of-the-art models in the GLUE benchmark by a significant margin. |
A Just and Comprehensive Strategy for Using NLP to Address Online Abuse (P19-1)
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| Challenge: | Current methods to detect online abuse focus on a narrow definition of abuse to detriment of victims seeking validation and solutions. |
| Approach: | They argue that the NLP community needs to make three substantive changes to tackle both more subtle and more serious forms of abuse. |
| Outcome: | The proposed approach would address the problem of abuse in a more inclusive and productive way. |
Learning from Dialogue after Deployment: Feed Yourself, Chatbot! (P19-1)
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| Challenge: | a majority of conversations a dialogue agent sees over its lifetime occur after it has already been trained and deployed, leaving a vast store of potential training signal untapped. |
| Approach: | They propose a self-feeding chatbot that extracts new training examples from conversations it participates in. |
| Outcome: | The proposed chatbot extracts training examples from conversations it participates in and predicts user satisfaction in its responses. |
Generating Responses with a Specific Emotion in Dialog (P19-1)
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| Challenge: | EmoDS can express emotions in both ways, but it is difficult to scale to large datasets. |
| Approach: | They propose an emotional dialog system that can express emotions in both ways . they use strong emotional words and neutral words to increase the intensity of emotions . |
| Outcome: | The proposed system performs better than baselines in BLEU, diversity and quality of emotional expression. |
Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention (P19-1)
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| Challenge: | Existing models for limited-domain RNNs are difficult to scale due to the complexity of the inputs. |
| Approach: | They propose to use dialog acts to build a multi-layer hierarchical graph with a disentangled self-attention network. |
| Outcome: | The proposed model improves on the baselines on automatic and human evaluation metrics. |
Incremental Learning from Scratch for Task-Oriented Dialogue Systems (P19-1)
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| Challenge: | Existing task-oriented dialogue systems cannot guarantee that all user needs are taken into account in the design phase. |
| Approach: | They propose a new incremental learning framework to design task-oriented dialogue systems without pre-defining user needs. |
| Outcome: | The proposed framework is robust to unconsidered user actions and can update itself online with less annotation cost. |
ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation (P19-1)
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| Challenge: | Existing hierarchical recurrent encoder-decoder models treat all contexts indiscriminately, which may hurt the following response generation process. |
| Approach: | They propose a hierarchical recurrent encoder-decoder model that treats all contexts indiscriminately and uses a word level LSTM encoder to obtain the initial representation of each context. |
| Outcome: | The proposed model outperforms baseline models on Chinese customer services and English Ubuntu dialogue datasets in terms of both metric-based and human evaluations. |
Dialogue Natural Language Inference (P19-1)
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| Challenge: | Consistency is a long standing issue faced by dialogue models. |
| Approach: | They propose to frame the consistency of dialogue agents as natural language inference and create a new natural language dataset called Dialogue NLI. |
| Outcome: | The proposed model can improve the consistency of a dialogue model with human evaluation and automatic metrics on a suite of evaluation sets designed to measure the model’s consistency. |
Budgeted Policy Learning for Task-Oriented Dialogue Systems (P19-1)
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| Challenge: | Existing approaches to integrating reinforcement learning into task-oriented dialogue systems require a fixed, small amount of user interactions to learn. |
| Approach: | They propose a budget-conscious scheduling approach that optimizes a fixed, small amount of user interactions for dialogue agent learning. |
| Outcome: | The proposed approach improves on a movie-ticket booking task with simulated and real users. |
Comparison of Diverse Decoding Methods from Conditional Language Models (P19-1)
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| Challenge: | Conditional language models can generate a diverse set of outputs, but for open-ended tasks, beam search is ill-suited to generating a set of diverse sequences. |
| Approach: | They propose a method where we over-sample candidates and use clustering to remove similar sequences to achieve high diversity without sacrificing quality. |
| Outcome: | The proposed method over-samples candidates and removes similar sequences to achieve high diversity without sacrificing quality. |
Retrieval-Enhanced Adversarial Training for Neural Response Generation (P19-1)
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| Challenge: | Existing approaches to dialogue systems are labor-intensive and difficult to scale up. |
| Approach: | They propose a Retrieval-Enhanced Adversarial Training method for neural response generation that leverages an adversarial training paradigm while taking advantage of N-best response candidates from a retrieval-based system to construct the discriminator. |
| Outcome: | The proposed method outperforms the vanilla Seq2Seq model and conventional adversarial training approach on a large scale dataset. |
Vocabulary Pyramid Network: Multi-Pass Encoding and Decoding with Multi-Level Vocabularies for Response Generation (P19-1)
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| Challenge: | Conventional methods employ a fixed vocabulary and one-pass decoding, which make them prone to safe and general responses and lack further refinement to the first generated raw sequence. |
| Approach: | They propose a Vocabulary Pyramid Network which integrates multi-pass encoding and decoding with multi-level vocabularies into response generation. |
| Outcome: | The proposed system outperforms strong baselines on English Twitter and Chinese Weibo datasets. |
On-device Structured and Context Partitioned Projection Networks (P19-1)
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| Challenge: | A challenge in on-device text classification is to build highly accurate models that fit in small memory footprint and have low latency. |
| Approach: | They propose an on-device neural network which learns compact projection vectors from raw text using structured and context-dependent partition projections. |
| Outcome: | The proposed model outperforms baseline models and surpasses RNN, CNN and BiLSTM models on dialog act and intent prediction. |
Proactive Human-Machine Conversation with Explicit Conversation Goal (P19-1)
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| Challenge: | Typical human-machine conversation systems only use utterances and responses as training data, which results in uninformative and inappropriate responses. |
| Approach: | They propose a dataset where one acts as a conversation leader and the other as 'follower' they establish baseline results on a 270K utterances and 30k dialogues dataset using state-of-the-art models. |
| Outcome: | The proposed model can generate diverse multi-turn conversations using knowledge from a new dataset . |
Learning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems (P19-1)
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| Challenge: | Existing methods for learning a robust matching model from noisy training data are retrieval-based or generation-based. |
| Approach: | They propose a general co-teaching framework that learns matching models from noisy training data. |
| Outcome: | The proposed learning framework can improve existing models on two public data sets. |
Learning to Abstract for Memory-augmented Conversational Response Generation (P19-1)
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| Challenge: | Existing generative models for open-domain chit-chat conversations lack informativeness and diversity. |
| Approach: | They propose a retrieval-augmented generative model that learns to abstract from the training corpus and saves useful information to the memory to assist the response generation. |
| Outcome: | The proposed model outperforms other baselines in query-response clustering and learning to utilize these characteristics for response generation. |
Are Training Samples Correlated? Learning to Generate Dialogue Responses with Multiple References (P19-1)
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| Challenge: | Existing approaches to open-domain dialogue generation ignore the nature of 1-to-1 mapping that there may exist multiple valid responses corresponding to the same query. |
| Approach: | They propose to model open-domain dialogue generation using 1-to-1 mapping . they first extract common features of different responses and then combine them with distinctive features to generate multiple diverse and appropriate responses. |
| Outcome: | The proposed model outperforms existing models on automatic and human evaluations. |
Pretraining Methods for Dialog Context Representation Learning (P19-1)
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| Challenge: | Existing methods for pretraining dialog context encoders are still in their infancy. |
| Approach: | They propose to use unsupervised pretraining objectives for dialog context representations to fine-tune and evaluate them on a set of downstream dialog tasks. |
| Outcome: | The proposed methods improve performance on a set of dialog tasks and are less data hungry. |
A Large-Scale Corpus for Conversation Disentanglement (P19-1)
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Jonathan K. Kummerfeld, Sai R. Gouravajhala, Joseph J. Peper, Vignesh Athreya, Chulaka Gunasekara, Jatin Ganhotra, Siva Sankalp Patel, Lazaros C Polymenakos, Walter Lasecki
| Challenge: | a dataset of 77,563 messages manually annotated with reply-structure graphs disentangles conversations and defines internal conversation structure. |
| Approach: | They use a dataset of 77,563 messages manually annotated with reply-structure graphs to disentangle conversations and define internal conversation structure. |
| Outcome: | The new dataset is 16 times larger than all previous datasets combined and includes adjudication of annotation disagreements and context. |
Self-Supervised Dialogue Learning (P19-1)
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| Challenge: | Existing dialogue systems have failed to capture the order of utterances in coherent dialogues. |
| Approach: | They propose a self-supervised learning task to capture the flow of dialogues . they propose 'inconsistent order detection' task to predict whether utterance is ordered or misordered . |
| Outcome: | The proposed methods can be applied to open-domain and task-oriented dialogue scenarios and achieve state-of-the-art performance on the OpenSubtitiles and Movie-Ticket Booking datasets. |
Are we there yet? Encoder-decoder neural networks as cognitive models of English past tense inflection (P19-1)
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| Challenge: | linguistics and cognitive science have long debated the cognitive mechanisms needed to account for the English past tense. |
| Approach: | They propose to use an encoder-decoder model to account for the english past tense . they also show that ED models demonstrate humanlike performance in a nonce-word task . |
| Outcome: | The proposed model is unstable across simulations and does not fit to human data . other neural models might do better, but there is insufficient evidence to claim them . |
A Spreading Activation Framework for Tracking Conceptual Complexity of Texts (P19-1)
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| Challenge: | Existing models for assessing conceptual complexity of texts are lacking . conceptual complexity accounts for background knowledge necessary to understand mentioned concepts . |
| Approach: | They propose an unsupervised approach for assessing conceptual complexity of texts based on spreading activation using DBpedia knowledge graph as a proxy to long-term memory. |
| Outcome: | The proposed model outperforms current state of the art in assessing conceptual complexity of texts. |
End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories (P19-1)
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| Challenge: | Existing sequence tagging models do not explicitly exploit linguistic theories of metaphor identification. |
| Approach: | They propose to exploit linguistic theories of metaphor identification in deep neural networks to improve model performance. |
| Outcome: | The proposed models achieve state-of-the-art in end-to-end metaphor identification on three datasets. |
Diachronic Sense Modeling with Deep Contextualized Word Embeddings: An Ecological View (P19-1)
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| Challenge: | Existing word embeddings only assign one vector to a word for a time period, thus they face the meaning conflation deficiency. |
| Approach: | They propose a sense representation and tracking framework based on deep contextualized embeddings that can be used to answer what and when the word meaning changes. |
| Outcome: | The proposed framework is effective in representing fine-grained word senses, and brings a significant improvement in word change detection task. |
Miss Tools and Mr Fruit: Emergent Communication in Agents Learning about Object Affordances (P19-1)
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| Challenge: | Recent research studies communication emergence in communities of deep network agents assigned a joint task . authors propose a game meeting many desiderata for a natural communication environment . |
| Approach: | They propose a task capturing aspects of the human environment and human conversation . they propose 'game' meeting many desiderata for a natural communication environment . |
| Outcome: | The proposed task captures aspects of human environment and human conversation, but the agents develop multiple idiolects, resulting in a common language. |
CNNs found to jump around more skillfully than RNNs: Compositional Generalization in Seq2seq Convolutional Networks (P19-1)
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| Challenge: | Recent deep neural network successes rekindled debates on their natural language processing abilities. |
| Approach: | They propose to test the ability of sequence-to-sequence networks to perform systematic, compositional generalization of linguistic rules. |
| Outcome: | The proposed dataset shows that convolutional networks perform better on compositional generalization tasks than RNNs. |
Uncovering Probabilistic Implications in Typological Knowledge Bases (P19-1)
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| Challenge: | linguistic typology is concerned with mapping out the relationships between languages with structural and functional properties. |
| Approach: | They propose a computational model which identifies known and new linguistic universals and uncovers them worthy of further linguistic investigation. |
| Outcome: | The proposed model outperforms baselines and knowledge base baselines. |
Is Word Segmentation Child’s Play in All Languages? (P19-1)
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| Challenge: | Existing word learning strategies for infants are cross-linguistically robust . infants do not know which language(s) will be found in their environment at the beginning of development . |
| Approach: | They propose to use 11 conceptually diverse algorithms to learn word-like units in infants . they propose to employ cross-linguistically robust algorithms that can be used by all infants. |
| Outcome: | The proposed algorithms perform above chance on 8 different languages . the results show that some of the algorithms are cross-linguistically valid . |
On the Distribution of Deep Clausal Embeddings: A Large Cross-linguistic Study (P19-1)
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| Challenge: | Empirical evidence on the prevalence and limits of embeddings has been based on either laboratory setups or corpus data of relatively limited size. |
| Approach: | They use large, dependency-parsed corpora to capture clausal embedding through dependency graphs and assess their distribution. |
| Outcome: | The results show that there is no evidence for hard constraints on embedding depth . they also show that sentences with many embeddable clauses do not display a bias towards less deep embedded sentences. |
Attention-based Conditioning Methods for External Knowledge Integration (P19-1)
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| Challenge: | Existing approaches for incorporating external knowledge into deep neural networks (RNNs) lexicon features are used to concatenate external information into the input or hidden network layers. |
| Approach: | They propose a method for conditioning external knowledge into RNNs by concatenating a representation of the external information to the input or hidden network layers. |
| Outcome: | The proposed approach improves performance on six benchmark datasets. |
The KnowRef Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution (P19-1)
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| Challenge: | Existing methods for coreference resolution exploit the number and gender of antecedents or have been handcrafted and do not reflect the diversity of naturally occurring text. |
| Approach: | They propose a trick to improve resolution by antecedent switching to target common-sense understanding and world knowledge. |
| Outcome: | The proposed method achieves state-of-the-art results on the GAP coreference task. |
StRE: Self Attentive Edit Quality Prediction in Wikipedia (P19-1)
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| Challenge: | Existing methods for content moderation and review rely on page reputation, editor activity or rule based heuristics. |
| Approach: | They propose a self-attentive revision encoder which leverages orthographic similarity of lexical units toward predicting the quality of new edits. |
| Outcome: | The proposed model outperforms existing models by at least 17% and at most 103% on a set of 21M revisions across 32K Wikipedia pages. |
How Large Are Lions? Inducing Distributions over Quantitative Attributes (P19-1)
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| Challenge: | Current NLP systems have little knowledge about quantitative attributes of objects and events. |
| Approach: | They propose to use web data to create a resource consisting of distributions over physical quantities associated with objects, adjectives, and verbs. |
| Outcome: | The proposed method compares favorably with state-of-the-art results on existing datasets for relative comparisons of nouns and adjectives and on a new dataset. |
Fine-Grained Sentence Functions for Short-Text Conversation (P19-1)
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| Challenge: | Existing research has analyzed various factors indicating the conversational purpose such as emotions, topics, word orders, syntactic patterns and other aspects. |
| Approach: | They propose to annotate a short-text conversation dataset with annotated sentences and train conversation models conditioned on the sentence functions. |
| Outcome: | The proposed model can predict the quality of the returned responses. |
Give Me More Feedback II: Annotating Thesis Strength and Related Attributes in Student Essays (P19-1)
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| Challenge: | Existing work on automated essay scoring has focused on holistic scoring, but there is limited annotated corpus of essays with thesis strength scores. |
| Approach: | They propose a scoring rubric for persuasive essay quality and annotate corpus of essays with thesis strength scores. |
| Outcome: | The proposed scoring rubric could provide feedback to students on why essay gets thesis strength score . the rubric can be used to score persuasive essay quality, thesis strength, and organization . |
Crowdsourcing and Validating Event-focused Emotion Corpora for German and English (P19-1)
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| Challenge: | Existing studies on automatic recognition of emotions in text have achieved promising results, but there is a shortage of resources for non-English languages, with few exceptions, like Chinese. |
| Approach: | They propose to use a crowdsourced German emotion corpus to build a corpus similar to the English ISEAR emotion dataset. |
| Outcome: | The proposed model performs well in German and English, but lacks the resources for non-English languages. |
Pay Attention when you Pay the Bills. A Multilingual Corpus with Dependency-based and Semantic Annotation of Collocations. (P19-1)
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| Challenge: | resulting corpus can be useful for different NLP tasks such as natural language understanding or natural language generation. |
| Approach: | They propose to annotate 155k tokens and 1,526 collocations in context in a multilingual corpus in English, Portuguese, and Spanish. |
| Outcome: | The new corpus can be used to evaluate different approaches for collocation identification, which can be useful for different NLP tasks such as natural language understanding or natural language generation. |
Does it Make Sense? And Why? A Pilot Study for Sense Making and Explanation (P19-1)
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| Challenge: | Existing benchmarks measure common sense knowledge indirectly or without reasoning. |
| Approach: | They propose a benchmark to test whether a system can differentiate natural language statements that make sense from those that do not make sense. |
| Outcome: | The proposed benchmarks show that models trained on large corpora perform better than humans on some benchmarks. |
Large Dataset and Language Model Fun-Tuning for Humor Recognition (P19-1)
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| Challenge: | Humor recognition datasets contain only English texts and focus on puns. |
| Approach: | They collected a dataset of jokes and funny dialogues in Russian and complemented them carefully with unfunny texts with similar lexical properties. |
| Outcome: | The proposed method is based on the universal language model finetuning and has an F1 score of 0.91 on a test set. |
Towards Language Agnostic Universal Representations (P19-1)
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| Challenge: | Current representations in machine learning are language dependent . however, fluent bilingual speakers rarely face trouble translating a task learned in one language to another . |
| Approach: | They propose a method to decouple the language from the problem by learning language agnostic representations. |
| Outcome: | The proposed model achieves similar accuracies in a single language and in another language. |
Leveraging Meta Information in Short Text Aggregation (P19-1)
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| Challenge: | Existing topic models infer topics based on word co-occurrence information, which results in degraded performance and degrades performance. |
| Approach: | They propose a generative model that aggregates short texts into clusters by leveraging the associated meta information. |
| Outcome: | The proposed model can generate more interpretable topics and document clusters. |
Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning (P19-1)
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| Challenge: | Encoder-decoder models for unsupervised sentence representation learning discard decoder after training . decoded sentences are often used to make better predictions of words in a given sentence . |
| Approach: | They propose two types of decoding functions whose inverse can be easily derived without expensive inverse calculation. |
| Outcome: | The proposed models can learn good representations from encoders and decoders without expensive calculations. |
Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text (P19-1)
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| Challenge: | Existing methods for unsupervised anomaly detection use pre-trained word embeddings . proper text representation is critical for designing well-performing machine learning algorithms . |
| Approach: | They propose a new anomaly detection method that builds upon word embedding models to learn multiple sentence representations that capture multiple semantic contexts via the self-attention mechanism. |
| Outcome: | The proposed method performs on Reuters, 20 Newsgroups, and IMDB Movie Reviews datasets. |
Hubless Nearest Neighbor Search for Bilingual Lexicon Induction (P19-1)
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| Challenge: | Existing methods for bilingual Lexicon Induction use nonparallel corpora, but hubness often degrades accuracy. |
| Approach: | They propose a method to create a lexicon of translation equivalents from non-parallel corpora by aligning two word embedding spaces and retrieving the nearest neighbor (NN) this method reduces hubness, which is necessary for retrieval tasks. |
| Outcome: | The proposed method outperforms NN, Inverted SoFtmax and other state-of-the-art methods. |
Distant Learning for Entity Linking with Automatic Noise Detection (P19-1)
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| Challenge: | Accurate entity linkers have been produced for domains and languages where no or very limited amounts of labeled data are available. |
| Approach: | They propose to use annotated text to learn to link entities without labeling . they frame the task as a multi-instance learning problem and rely on surface matching to create initial noisy labels. |
| Outcome: | The proposed method outperforms the baseline surface matching model for a subset of entities. |
Learning How to Active Learn by Dreaming (P19-1)
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| Challenge: | Recent active learning methods are limited when the data distribution of learning problems vary. |
| Approach: | They propose a wake-and-dream-based active learning method that learns the AL policy directly on the target domain of interest by using wake and dream cycles. |
| Outcome: | The proposed method improves on cross-domain and cross-lingual tasks. |
Few-Shot Representation Learning for Out-Of-Vocabulary Words (P19-1)
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| Challenge: | Existing methods for learning word embedding assume there are enough occurrences for each word in the corpus to accurately estimate the representation of words. |
| Approach: | They propose to fit a representation function to predict an oracle embedding vector based on limited contexts. |
| Outcome: | The proposed model outperforms existing methods in constructing an accurate embedding for OOV words and improves downstream tasks when the embeddable is utilized. |
Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models (P19-1)
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| Challenge: | Recent studies show that document classifiers can become more stable over time when trained in ways that account for temporal variations. |
| Approach: | They propose a method for embedding diachronic word embedds into document classification models . they propose 'time-driven neural classification model' that accounts for temporal variations . |
| Outcome: | The proposed model can be trained on six corpora and make it more robust over time. |
Learning Transferable Feature Representations Using Neural Networks (P19-1)
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| Challenge: | Traditional domain adaptation algorithms learn common representations which suffer from transfer loss when the source specific characteristics detract their ability to represent the target data. |
| Approach: | They propose to segregate source specific representation from the common representation and use it to learn a two-part representation which captures source specific characteristics while the second part captures the truly common representation. |
| Outcome: | The proposed representation outperforms existing learning algorithms on the source learning as well as cross-domain tasks on multiple datasets. |
Bayes Test of Precision, Recall, and F1 Measure for Comparison of Two Natural Language Processing Models (P19-1)
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| Challenge: | Existing t-tests for cross-validation (CV) are inappropriate for model comparison . existing t tests for cross validation (CV), such as 52 CV t test and F ttest, are inadequate . |
| Approach: | They propose to use a block-regularized 32 CV to compare two NLP models . they calibrate the posterior distributions of P, R, and F1 and derive an accurate interval estimation of P and R . |
| Outcome: | The proposed model could regularize the difference in certain frequency distributions over linguistic units and yield stable estimators of P, R, and F1. |
TIGS: An Inference Algorithm for Text Infilling with Gradient Search (P19-1)
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| Challenge: | Text infilling is an under-explored challenge in the field of text generation. |
| Approach: | They propose an iterative inference algorithm based on gradient search that can be broadly applied to any sequence generative model for text infilling tasks. |
| Outcome: | The proposed method performs well on three different text infilling tasks with different mask ratios and mask strategies compared with five state-of-the-art methods. |
Keeping Notes: Conditional Natural Language Generation with a Scratchpad Encoder (P19-1)
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| Challenge: | Qualitative assessments in the form of human judgements (question generation), attention visualization (MT), and sample output (summarization) provide further evidence of the ability of Scratchpad to generate fluent and expressive output. |
| Approach: | They propose to use the encoder as a "scratchpad" memory to keep track of what has been generated and guide future generation. |
| Outcome: | The proposed mechanism improves the fluency of seq2seq models on three well-studied natural language generation tasks. |
Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection (P19-1)
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| Challenge: | Existing methods to evaluate maximum spans tangle coreference evaluation with mention boundary detection . however, this method is costly and does not scale to large corpora. |
| Approach: | They propose an algorithm for automatically extracting minimum spans to benefit from minimum span evaluation in all corpora. |
| Outcome: | The proposed algorithm is consistent with those manually annotated by experts. |
Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution (P19-1)
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| Challenge: | Recognizing that various textual spans across multiple texts refer to the same entity or event is an important NLP task. |
| Approach: | They propose a neural architecture for cross-document coreference resolution by representing an event mention using its lexical span, surrounding context, and relation to other mentions via predicate-arguments structures. |
| Outcome: | The proposed model outperforms the state-of-the-art event coreference model on ECB+ while providing the first entity coreference results on this corpus. |
A Unified Linear-Time Framework for Sentence-Level Discourse Parsing (P19-1)
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| Challenge: | a new neural framework for sentence-level discourse analysis is proposed . a discourse segmenter and a parser are based on pointer networks and operate in linear time . |
| Approach: | They propose a neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory . they use a discourse segmenter and a parser to construct a discursive tree in a top-down fashion . |
| Outcome: | The proposed framework surpasses previous approaches on both tasks and human agreement on both. |
Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings (P19-1)
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| Challenge: | Existing models for implicit discourse relation recognition lack the ability to accurately map connectives into discourse relations. |
| Approach: | They propose a multi-task learning framework where relations and connectives are simultaneously predicted and leveraged to transfer knowledge between the two prediction tasks. |
| Outcome: | The proposed framework yields state-of-the-art performance on several settings of the Penn Discourse Treebank dataset. |
Do You Know That Florence Is Packed with Visitors? Evaluating State-of-the-art Models of Speaker Commitment (P19-1)
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| Challenge: | Existing models for speaker commitment fail to generalize to diverse linguistic constructions, highlighting directions for improvement. |
| Approach: | They evaluate two state-of-the-art speaker commitment models on the CommitmentBank . they analyze linguistic correlates of model error on a naturalistic dataset . |
| Outcome: | The proposed models perform well on some classes but fail to generalize to diverse linguistic constructions. |
Multi-Relational Script Learning for Discourse Relations (P19-1)
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| Challenge: | Existing script knowledge models only represent a single event relationship, co-occurrence . this is coarse for commonsense, which should account for fine-grained relationships . |
| Approach: | They propose to view learning event embedding as a multi-relational problem . they model a rich set of event relations derived from the Penn Discourse Tree Bank . |
| Outcome: | The proposed model captures different aspects of event pairs, including cause and contrast. |
Open-Domain Why-Question Answering with Adversarial Learning to Encode Answer Texts (P19-1)
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| Challenge: | Existing why-QA methods retrieve “answer passages” that consist of several sentences . AGR is a vector representation of the non-redundant reason sought by a why-question . |
| Approach: | They propose a method for why-question answering that uses an adversarial learning framework. |
| Outcome: | The proposed method improves state-of-the-art open-domain QA on Japanese datasets . it also improves a state- of-the art method on publicly available English datasets. |
Learning to Ask Unanswerable Questions for Machine Reading Comprehension (P19-1)
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| Challenge: | Existing models for extractive reading comprehension are not good at deciding whether no answer is presented in the context. |
| Approach: | They propose a data augmentation technique by automatically generating relevant unanswerable questions according to an answerable question paired with its corresponding paragraph that contains the answer. |
| Outcome: | The proposed model performs better on the SQuAD 2.0 dataset than the baseline model and the BERT-large model. |
Compositional Questions Do Not Necessitate Multi-hop Reasoning (P19-1)
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| Challenge: | a single-hop reasoning model can solve much more of the dataset than previously thought. |
| Approach: | They propose a single-hop BERT-based RC model that achieves 67 F1 . they propose an evaluation setting where humans are not shown all paragraphs . |
| Outcome: | The proposed model achieves 67 F1—comparable to state-of-the-art multi-hop models. |
Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader (P19-1)
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| Challenge: | Existing models that use incomplete knowledge bases and text data to answer open-domain questions are insufficient to cover full evidence. |
| Approach: | They propose a model which learns to aggregate answer evidence from incomplete knowledge bases and text snippets. |
| Outcome: | The proposed model improves on the widely-used KBQA benchmark WebQSP across settings with different extents of incompleteness. |
AdaNSP: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing (P19-1)
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| Challenge: | Semantic parsing (SP) maps a natural language utterance into a formal language . standard Seq2Seq models ignore underlying grammars and may give ill-formed results. |
| Approach: | They propose an end-to-end model for semantic parsing that transduces a natural language sentence to the formal semantic representation. |
| Outcome: | The proposed model outperforms the state-of-the-art models and does not need expertise like predefined grammar or sketches in the meantime. |
The Language of Legal and Illegal Activity on the Darknet (P19-1)
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| Challenge: | a study of the characteristics of text in the Darknet shows that it has legal and illegal content. |
| Approach: | They compare texts for selling legal and illegal drugs to a control condition . they find several distinguishing features between legal and illicit texts . |
| Outcome: | The authors compare legal and illegal texts to a clear net website with similar content as a control condition. |
Eliciting Knowledge from Experts: Automatic Transcript Parsing for Cognitive Task Analysis (P19-1)
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| Challenge: | Cognitive task analysis (CTA) is a type of analysis used to elicit and represent the knowledge and thought processes of domain experts. |
| Approach: | They propose a weakly-supervised framework for automated CTA transcript parsing . they partition the parser process into a sequence labeling task and a text span-pair relation extraction task with distant supervision from human-curated protocol files. |
| Outcome: | The proposed framework reduces human labor and scales the task to a small scale. |
Course Concept Expansion in MOOCs with External Knowledge and Interactive Game (P19-1)
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| Challenge: | Existing methods to expand course concepts in MOOCs suffer from semantic drifts and lack of knowledge guidance. |
| Approach: | They propose to use a boundary search method to search for new concepts via external knowledge base and then use heterogeneous features to verify the results. |
| Outcome: | The proposed method improves on the datasets from Coursera and XuetangX. |
Towards Near-imperceptible Steganographic Text (P19-1)
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| Challenge: | Existing methods for linguistic steganography are vulnerable to automated detection. |
| Approach: | They propose an encoding algorithm with improved near-imperceptible guarantees based on implicit assumptions on statistical behaviors of fluent text. |
| Outcome: | The proposed algorithm improves on existing steganographic systems with near-imperceptible guarantees. |
Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network (P19-1)
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| Challenge: | Existing methods for inter-sentence relation extraction do not fully exploit such dependencies. |
| Approach: | They propose a model that captures local and non-local dependencies using multi-instance learning and bi-affine pairwise scoring to predict the relation of an entity pair. |
| Outcome: | The proposed model performs comparable to state-of-the-art models on biochemistry datasets. |
Neural Legal Judgment Prediction in English (P19-1)
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| Challenge: | Recent work on legal judgment prediction has focused on Chinese, but only feature-based models have been considered in English. |
| Approach: | They propose a hierarchical version of BERT which bypasses BERT’s length limitation. |
| Outcome: | The proposed model outperforms existing models in binary violation classification, multi-label classification and case importance prediction. |
Robust Neural Machine Translation with Doubly Adversarial Inputs (P19-1)
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| Challenge: | Neural machine translation (NMT) models suffer from noisy perturbations in the input . a gradient-based method to craft adversarial examples informed by the translation loss is proposed . |
| Approach: | They propose an approach to improve the robustness of NMT models by attacking the translation model with adversarial source examples and defending the model with a target input. |
| Outcome: | The proposed approach improves translation performance and robustness on clean inputs and higher on noisy data. |
Bridging the Gap between Training and Inference for Neural Machine Translation (P19-1)
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| Challenge: | Neural Machine Translation generates target words sequentially while at inference it has to generate the entire sequence from scratch. |
| Approach: | They propose to use ground truth and inference to generate target words sequentially while at inference it has to generate the entire sequence from scratch. |
| Outcome: | Experiments on Chinese->English and WMT’14 English->German translation tasks show that the proposed model can achieve significant improvements on multiple datasets. |
Beyond BLEU:Training Neural Machine Translation with Semantic Similarity (P19-1)
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| Challenge: | Recent work has shown that optimizing neural machine translation systems to directly improve evaluation metrics such as BLEU can improve final translation accuracy. |
| Approach: | They propose a reward function that assigns partial credit to BLEU and provides more diversity in scores than BLUE. |
| Outcome: | The proposed reward function improves translation accuracy, semantic similarity, and human evaluation on four languages trans-lated to English and the optimization procedure converges faster. |
AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text (P19-1)
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| Challenge: | Recent advances in Automatic Machine Learning (AutoML) provide effective tools to explore large sets of algorithms, hyper-parameters and features to find out the best combination of them. |
| Approach: | They propose a novel AutoML strategy based on probabilistic grammatical evolution to explore the best combination of parameters and features to use when dealing with the knowledge discovery challenge in Spanish text documents. |
| Outcome: | The proposed strategy achieves state-of-the-art and provides interesting insights into the best combination of parameters and algorithms to use when dealing with this challenge. |
Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning (P19-1)
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| Challenge: | Current neural event detection approaches focus on trigger-centric representations, which work well on distilling discrimination knowledge, but poorly on learning generalization knowledge. |
| Approach: | They propose a Delta-learning approach to distill discrimination and generalization knowledge by incrementally learning and adaptively fusing event representation. |
| Outcome: | The proposed method significantly outperforms previous approaches on unseen/sparse trigger words and achieves state-of-the-art performance on ACE2005 and KBP2017 datasets. |
Chinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge (P19-1)
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| Challenge: | Existing methods for Chinese relation extraction suffer from segmentation errors and ambiguity of polysemy. |
| Approach: | They propose a multi-grained lattice framework for Chinese relation extraction . they incorporate word-level information into character sequence inputs to avoid segmentation errors . |
| Outcome: | The proposed model outperforms existing models on three real-world datasets in distinct domains. |
A2N: Attending to Neighbors for Knowledge Graph Inference (P19-1)
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| Challenge: | Existing knowledge graph completion methods learn a fixed embedding for every entity, which is suboptimal as it requires memorizing and generalizing to all possible entity relationships. |
| Approach: | They propose a method which learns query-dependent representations of entities by combining relevant neighborhood of an entity. |
| Outcome: | The proposed model performs competitively or better than existing state-of-the-art models for knowledge graph completion. |
Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures (P19-1)
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| Challenge: | Existing work on event factuality prediction (EFP) relies on syntactic and semantic information to identify important context words. |
| Approach: | They propose a graph-based neural network that integrates syntactic and semantic information more effectively. |
| Outcome: | The proposed model integrates syntactic and semantic information more effectively . it provides more meaningful information for downstream tasks than classification formulations . |
Embedding Time Expressions for Deep Temporal Ordering Models (P19-1)
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| Challenge: | Existing data-driven models fail to capture explicit temporal signals, such as dates and time windows. |
| Approach: | They propose a framework to infuse temporal awareness into data-driven models by learning a pre-trained model to embed timexes. |
| Outcome: | The proposed framework infuses temporal awareness into data-driven models by learning a pre-trained model to embed timexes. |
Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming Data (P19-1)
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| Challenge: | Existing QA methods lack scalability and performance is difficult to solve with document-level contexts. |
| Approach: | They propose an end-to-end deep network model that sequentially reads the input contexts into an external memory while replacing memories that are less important for answering unseen questions. |
| Outcome: | The proposed model improves on a synthetic dataset and real-world large-scale textual and video QA datasets. |
Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets (P19-1)
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| Challenge: | Natural Language Sentence Matching (NLSM) is a popular NLP task. |
| Approach: | They propose to use QuoraQP to train and evaluate NLSM models using a selection bias framework. |
| Outcome: | The proposed framework can improve generalization ability of trained models and give more trustworthy evaluation results for real-world adoptions. |
Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index (P19-1)
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| Challenge: | Existing open-domain question answering models require multiple documents on-demand for every input query. |
| Approach: | They propose query-agnostic indexable representations of document phrases that can drastically speed up open-domain question answering. |
| Outcome: | The proposed model can be trained and deployed even in a single 4-GPU server. |
Language Modeling with Shared Grammar (P19-1)
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| Challenge: | Recent work on structure-aware models have shown promising results on language modeling, but how to incorporate structure knowledge on corpus without syntactic annotations remains an open problem. |
| Approach: | They propose a neural variational language model which enables the sharing of grammar knowledge among different corpora. |
| Outcome: | The proposed model converges significantly faster to lower perplexity on two popular benchmark datasets. |
Zero-Shot Semantic Parsing for Instructions (P19-1)
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| Challenge: | Recent years have seen an increasing number of applications that have a natural language interface, such as chatbots or "intelligent personal assistants" |
| Approach: | They propose a new training algorithm that trains a semantic parser on examples from a set of source domains and augment it with features and a logical form candidate filtering logic to support zero-shot adaptation. |
| Outcome: | The proposed framework performs better than a non-adapted parser with features and logical form candidate filtering logic. |
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling (P19-1)
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Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman
| Challenge: | State-of-the-art models in natural language processing (NLP) often incorporate sentence encoder functions which generate a sequence of vectors intended to represent the in-context meaning of each word in an input text. |
| Approach: | They conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks as alternatives and complements to language modeling. |
| Outcome: | The proposed model can be used to train sentences on language modeling tasks. |
Complex Question Decomposition for Semantic Parsing (P19-1)
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| Challenge: | Existing methods that ignore the decompositionality of complex questions are not suitable for complex question semantic parsing. |
| Approach: | They propose a hierarchical semantic parsing method which utilizes the decompositionality of complex questions for semantic paring. |
| Outcome: | The proposed method improves on a large scale complex question semantic parsing dataset. |
Multi-Task Deep Neural Networks for Natural Language Understanding (P19-1)
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| Challenge: | Existing approaches to learning vector-space representations of text are multitask learning and language model pre-training. |
| Approach: | They propose a multi-task deep neural network (MT-DNN) that leverages cross-task data and incorporates a pre-trained bidirectional transformer language model. |
| Outcome: | The proposed model achieves state-of-the-art on ten NLU tasks and pushes the GLUE benchmark to 82.7% (2.2% absolute improvement) |
DisSent: Learning Sentence Representations from Explicit Discourse Relations (P19-1)
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| Challenge: | Existing models train on vast amounts of text or require costly, manually curated datasets. |
| Approach: | They propose to leverage the discourse relations between sentences to curate a high quality sentence relation task by leveraging explicit discourse relations. |
| Outcome: | The proposed model can be used to learn the meaning of two sentences in a bidirectional LSTM sentence encoder. |
SParC: Cross-Domain Semantic Parsing in Context (P19-1)
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Tao Yu, Rui Zhang, Michihiro Yasunaga, Yi Chern Tan, Xi Victoria Lin, Suyi Li, Heyang Er, Irene Li, Bo Pang, Tao Chen, Emily Ji, Shreya Dixit, David Proctor, Sungrok Shim, Jonathan Kraft, Vincent Zhang, Caiming Xiong, Richard Socher, Dragomir Radev
| Challenge: | Xu et al., 2017): a dataset for cross-domain semantic parsing in context with 4,298 question sequences. |
| Approach: | They present a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences. |
| Outcome: | The proposed dataset demonstrates that it has greater semantic diversity and can be generalized to unseen domains due to its cross-domain nature and the unseened databases at test time. |
Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation (P19-1)
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| Challenge: | IRNet synthesizes SQL queries in an end-to-end manner, but it yields unsatisfactory performance on public benchmarks. |
| Approach: | They propose a neural approach called IRNet for complex and cross-domain Text-to-SQL. |
| Outcome: | IRNet achieves 46.7% accuracy on the Spider benchmark, a 19.5% improvement over state-of-the-art approaches. |
EigenSent: Spectral sentence embeddings using higher-order Dynamic Mode Decomposition (P19-1)
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| Challenge: | Existing methods for calculating word embeddings use weighted averages or learn word representations, but this is not the case. |
| Approach: | They propose to use spectral methods of signal representation and summarization to construct word-sequence embeddings in unsupervised fashion by using a spectrally-based decomposition and signal summarizing technique on text. |
| Outcome: | The proposed method can summarize transitions in a sequence of words and generate an embedding that can represent well the sequence itself. |
SemBleu: A Robust Metric for AMR Parsing Evaluation (P19-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic formalism where the meaning of a sentence is encoded as a rooted, directed graph. |
| Approach: | They propose a metric that extends SMATCH to parse AMRs and does not suffer from search errors. |
| Outcome: | The proposed metric does not suffer from search errors and considers non-local correspondences in addition to local ones. |
Reranking for Neural Semantic Parsing (P19-1)
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| Challenge: | Semantic parsing is the task of transducing natural language utterances into machine executable meaning representations (e.g., Python code). |
| Approach: | They propose to rerank an n-best list of predicted MRs and use features to fix observed problems with baseline models to improve parser performance. |
| Outcome: | The proposed method outperforms the best published neural parser on four datasets and improves the baseline parsing performance by 5.7% and 2.9%. |
Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing (P19-1)
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| Challenge: | Semantic parsing to SQL has largely ignored the structure of the database schema . a recent study used a simple DB that was observed at both training and test time. |
| Approach: | They propose a semantic parser where the schema structure is encoded with a graph neural network and used at both encoding and decoding time. |
| Outcome: | The proposed parser improves from 33.8% to 39.4%, dramatically above the current state of the art, which is at 19.7%. |
Human vs. Muppet: A Conservative Estimate of Human Performance on the GLUE Benchmark (P19-1)
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| Challenge: | GLUE is a suite of language understanding tasks that has seen dramatic progress in the past year . average performance on the benchmark is 83.9, state of the art at the time of writing . |
| Approach: | They use crowdsourcing to measure human performance on a set of language understanding tasks and 20 examples to determine whether there is room for improvement. |
| Outcome: | The GLUE benchmark outperforms state-of-the-art models on six of the nine tasks and achieves an average score of 87.1. |
Compositional Semantic Parsing across Graphbanks (P19-1)
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| Challenge: | Existing semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. |
| Approach: | They propose a compositional neural semantic parser which achieves competitive accuracies across graphbanks. |
| Outcome: | The proposed system achieves competitive accuracies across a variety of graphbanks. |
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning (P19-1)
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| Challenge: | Abstract meaning representations (AMRs) are labeled directed acyclic graphs that represent a non intersentential abstraction of natural language with broad-coverage semantic representations. |
| Approach: | They build upon a transition-based AMR parser that uses Stack-LSTMs and augment training with policy learning. |
| Outcome: | The proposed parser performs comparable to the best published parsers. |
BERT Rediscovers the Classical NLP Pipeline (P19-1)
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| Challenge: | Pre-trained text encoders have advanced the state of the art on many NLP tasks . Qualitative analysis reveals that the model can and often does adjust this pipeline dynamically . |
| Approach: | They aim to quantify where linguistic information is captured within a network model . they aim to use pre-trained text encoders to displace static word embeddings . |
| Outcome: | The proposed model can adjust the pipeline dynamically, revealing lower-level decisions on the basis of disambiguation from higher-level representations. |
Simple and Effective Paraphrastic Similarity from Parallel Translations (P19-1)
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| Challenge: | Existing methods for learning paraphrastic sentence embeddings on bitext are expensive and require manual annotation. |
| Approach: | They propose a method that trains paraphrastic sentence embeddings directly from bitext, eliminating the time-consuming step of creating paraphrase corpora. |
| Outcome: | The proposed model outperforms and is faster than state-of-the-art models on cross-lingual tasks. |
Second-Order Semantic Dependency Parsing with End-to-End Neural Networks (P19-1)
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| Challenge: | Existing approaches to semantic dependency parsing are graph-based and transition-based. |
| Approach: | They propose a second-order semantic dependency parser which takes relationships between two or more edges into account. |
| Outcome: | The proposed algorithm outperforms existing approaches to parsing on graph-based approaches. |
Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper) (P19-1)
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Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, Roger Zimmermann, Rada Mihalcea, Soujanya Poria
| Challenge: | sarcasm is often expressed through multiple verbal and non-verbal cues, such as a change of tone, overemphasis, drawn-out syllables, or a straight looking face. |
| Approach: | They propose to use multimodal cues to improve sarcasm detection using audiovisual utterances annotated with sarcasm labels to improve the accuracy. |
| Outcome: | The proposed dataset reduces the error rate of sarcasm detection by 12.9% . it is based on audiovisual utterances annotated with sarcasm labels . |
Determining Relative Argument Specificity and Stance for Complex Argumentative Structures (P19-1)
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| Challenge: | Existing work on claim specificity and stance has been limited to shallow arguments . a system that can determine the stance of claims employed in argumentation is not sufficient . |
| Approach: | They propose to use a dataset of manually curated argument trees to study claim specificity and stance in argumentation. |
| Outcome: | The proposed dataset consists of manually curated argument trees for 741 controversial topics covering 95,312 unique claims. |
Latent Variable Sentiment Grammar (P19-1)
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| Challenge: | Existing neural models do not explicitly model sentiment composition, which requires to encode sentiment class labels. |
| Approach: | They propose a sentiment grammar that captures sentiment subtype expressions by latent variables and Gaussian mixture vectors. |
| Outcome: | The proposed model outperforms vanilla neural encoders on the Stanford Sentiment Treebank benchmark. |
An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese (P19-1)
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| Challenge: | Text-based transfer learning techniques can be used to perform downstream tasks. |
| Approach: | They propose to use text-based transfer learning techniques to pre-train a language model in an unsupervised manner and leverage them to perform effective on downstream tasks. |
| Outcome: | The proposed model performs better than task-specific models trained on 3 times as much data and is as effective for language modeling pre-trained on 1/30 of the data. |
Probing Neural Network Comprehension of Natural Language Arguments (P19-1)
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| Challenge: | Argument Reasoning Comprehension Task (ARCT) focuses on inferences, not just discovering warrants. |
| Approach: | They propose to build an adversarial dataset on which all models achieve random accuracy. |
| Outcome: | The proposed dataset provides a more robust assessment of argument comprehension and should be adopted as the standard in future work. |
Recognising Agreement and Disagreement between Stances with Reason Comparing Networks (P19-1)
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| Challenge: | Existing methods for (dis)agreement detection focus on conversational settings . however, non-dialogic stance-bearing utterances are common in real-world scenarios . |
| Approach: | They propose a reason comparing network to leverage reason information for stance comparison. |
| Outcome: | The proposed method outperforms baselines on a well-known stance corpus. |
Toward Comprehensive Understanding of a Sentiment Based on Human Motives (P19-1)
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| Challenge: | a new study examines the motivations of sentiment holder and their relationship to food . aspects of a sentiment are limited to properties of entities such as the price of food and design of . |
| Approach: | They define six basic motives that cover a wide range of topics appearing in review texts . they annotate 1,600 texts in restaurant and laptop domains with the motives . |
| Outcome: | The proposed method improves on annotating 1,600 text with the motives and comparing them to other methods. |
Context-aware Embedding for Targeted Aspect-based Sentiment Analysis (P19-1)
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| Challenge: | Existing methods do not specifically pre-train reasonable embeddings for targets and aspects in TABSA. |
| Approach: | They propose to refine the embeddings of targets and aspects using a sparse coefficient vector . this allows the embeds to be refined from highly correlative words instead of context-independent vectors . |
| Outcome: | Experiments show that the proposed method improves on two benchmark datasets. |
Yes, we can! Mining Arguments in 50 Years of US Presidential Campaign Debates (P19-1)
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| Challenge: | Political debates are a natural application scenario for Argument Mining. |
| Approach: | They propose an argument mining approach to political debates that uses argument components to annotate 39 political debate from the last 50 years of US presidential campaigns. |
| Outcome: | The proposed approach outperforms baselines in argument mining over political debates. |
An Empirical Study of Span Representations in Argumentation Structure Parsing (P19-1)
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Tatsuki Kuribayashi, Hiroki Ouchi, Naoya Inoue, Paul Reisert, Toshinori Miyoshi, Jun Suzuki, Kentaro Inui
| Challenge: | Argumentation structure parsing (ASP) is a task of identifying argumentation structures in argumentative text. |
| Approach: | They propose to exploit neural network-based span representations for ASP to improve performance . they also propose task-dependent extensions for a parser that can be used to parse arguments . |
| Outcome: | The proposed model outperforms neural network-based approaches for argumentation structure parsing (ASP) it also provides some challenging types of instances to be parsed. |
Simple and Effective Text Matching with Richer Alignment Features (P19-1)
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| Challenge: | Existing models only use a single inter-sequence alignment layer to make full use of this process. |
| Approach: | They propose to keep three key features available for inter-sequence alignment . they conduct experiments on four well-studied benchmark datasets . |
| Outcome: | The proposed model is able to perform on four well-studied datasets with fewer parameters and the inference speed is at least 6 times faster than similar models. |
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs (P19-1)
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| Challenge: | Existing knowledge graphs (KGs) are incomplete or partial information, in the form of missing relations between entities, which gives rise to the task of knowledge base completion (also known as relation prediction). |
| Approach: | They propose to capture both entity and relation features in any given neighborhood and encapsulate relation clusters and multi-hop relations in their attention-based model. |
| Outcome: | The proposed model captures both entity and relation features in any given neighborhood and also encapsulates relation clusters and multi-hop relations. |
Neural Network Alignment for Sentential Paraphrases (P19-1)
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| Challenge: | a monolingual alignment system is ill-suited for word- or short phrase-based alignments. |
| Approach: | They propose a monolingual alignment system for long, sentence- or clause-level alignments . they show that systems designed for word- or short phrase-based alignment are ill-suited for longer alignments. |
| Outcome: | The proposed system outperforms state-of-the-art systems on long alignments . it achieves significantly higher recall on aligning phrases of four or more words . |
Duality of Link Prediction and Entailment Graph Induction (P19-1)
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| Challenge: | In this paper, we show that link prediction and entailment graph induction are complementary. |
| Approach: | They propose an entailment score that exploits the new facts discovered by the link prediction model and then form engorgement graphs between relations. |
| Outcome: | The proposed entailment score outperforms prior state-of-the-art results on a standard entialment dataset and the new link prediction scores show improvements over the raw link prediction score. |
A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching (P19-1)
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| Challenge: | Existing approaches to text matching consider each sequence separately . a proposed model uses both sequences to generate a given relationship with a source sequence . |
| Approach: | They propose a latent variable model for predicting the relationship between a pair of text sequences by generating a sequence that has a given relationship with a source sequence. |
| Outcome: | The proposed model achieves state-of-the-art on natural language inference and paraphrase identification. |
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction (P19-1)
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| Challenge: | Existing studies on commonsense knowledge base construction only store loosely structured open-text descriptions of knowledge. |
| Approach: | They propose a commonsense knowledge base construction model that generates rich commonsensense descriptions in natural language. |
| Outcome: | The proposed models can generate rich and diverse commonsense descriptions in natural language. |
Detecting Subevents using Discourse and Narrative Features (P19-1)
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| Challenge: | Existing models for detecting events as subevents have been developed for analyzing textual understanding. |
| Approach: | They propose a supervised model that automatically identifies when one event is a subevent of another. |
| Outcome: | The proposed model outperforms previous systems on two annotated corpora with event hierarchies, achieving 0.74 BLANC F1 on the Intelligence Community corpus and 0.70 F1 for the HiEve corpus, respectively a 15 and 5 percentage point improvement over previous models. |
HellaSwag: Can a Machine Really Finish Your Sentence? (P19-1)
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| Challenge: | Existing commonsense models struggle to perform inferences that are trivial for humans, but are often misclassified by state-of-the-art models. |
| Approach: | They propose a dataset that is adversarial to state-of-the-art commonsense reasoning and use it to build a model that is surprisingly robust. |
| Outcome: | The proposed dataset is compared with existing models and scaled up towards a critical 'Goldilocks zone' wherein generated text is ridiculous to humans, yet often misclassified by state-of-the-art models. |
Unified Semantic Parsing with Weak Supervision (P19-1)
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Priyanka Agrawal, Ayushi Dalmia, Parag Jain, Abhishek Bansal, Ashish Mittal, Karthik Sankaranarayanan
| Challenge: | Semantic parsing over multiple knowledge bases requires high-quality annotations of (utterance, program) pairs. |
| Approach: | They propose a framework to build a unified multi-domain enabled semantic parser with weak supervision. |
| Outcome: | The proposed model improves performance by 20% on the Overnight dataset. |
Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings (P19-1)
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| Challenge: | Existing approaches to domain-specific taxonomy induction from text are relying on distributional semantics for hyponym-hypernym relationships, but many of them learn prototypical hypernymes, not taking into account the relation between both terms in classification. |
| Approach: | They propose to use Poincaré embeddings to improve existing approaches to domain-specific taxonomy induction from text as a signal for relocating wrong hyponym terms and attaching disconnected terms in a taxonomies. |
| Outcome: | The proposed method significantly improves state-of-the-art methods on the SemEval-2016 Task 13 on taxonomy extraction. |
Learning to Rank for Plausible Plausibility (P19-1)
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| Challenge: | Existing approaches to encoding sentences using contextualized encoders are inconsistent . |
| Approach: | They propose to use a cross entropy log-loss objective to improve plausibility . they propose a margin-based loss leads to a more plausible model of plausability . |
| Outcome: | The proposed loss is intuitively wrong when applied to plausibility tasks . the proposed loss leads to a more plausible model of plausability . |
Generalized Tuning of Distributional Word Vectors for Monolingual and Cross-Lingual Lexical Entailment (P19-1)
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| Challenge: | Lexical entailment (LE) is a core asymmetric lexical relation that supports tasks like taxonomy induction and text generation. |
| Approach: | They propose a generalized Lexical ENtailment model that captures a specialization function allowing for LE-tuning of the entire distributional vector space and not only the vectors of words seen in lexical constraints. |
| Outcome: | The proposed model improves on graded LE and shows 20% improvement over state-of-the-art LE detection. |
Attention Is (not) All You Need for Commonsense Reasoning (P19-1)
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| Challenge: | Recent language models such as word2vec have produced impressive results on various tasks such as question-answering and natural language inference. |
| Approach: | They propose a simple re-implementation of BERT for commonsense reasoning . they propose to use attention-guided reasoning to solve the Pronoun Disambiguation Problem . |
| Outcome: | The proposed model outperforms the state-of-the-art on several language understanding benchmarks while outperforming the existing models by a margin. |
A Surprisingly Robust Trick for the Winograd Schema Challenge (P19-1)
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| Challenge: | The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning. |
| Approach: | They propose to fine-tune language models on the Winograd Schema Challenge dataset WSC273 and its inference counterpart WNLI to achieve accuracies of 72.5% and 74.7%, respectively. |
| Outcome: | The proposed language models achieve 72.5% and 74.7% accuracy on the WSC273 and WNLI datasets, respectively. |
Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model (P19-1)
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| Challenge: | Existing models for article comment generation are too long and often result in general and irrelevant comments. |
| Approach: | They propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph. |
| Outcome: | The proposed model can generate coherent and informative comments compared with several strong baseline models. |
Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling (P19-1)
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| Challenge: | Extensive experiments show that our system outperforms several baselines and can generate highly conversational questions. |
| Approach: | They propose a neural model that generates interconnected questions in question-answering style conversations. |
| Outcome: | The proposed model outperforms baselines and can generate highly conversational questions. |
Cross-Lingual Training for Automatic Question Generation (P19-1)
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| Challenge: | Automatic question generation is a challenging problem in natural language understanding . manual curating a dataset of comparable size for a new language is tedious and expensive. |
| Approach: | They propose to reuse available large QG dataset in a secondary language to learn a QG model for a primary language. |
| Outcome: | The proposed model outperforms baseline models in Hindi and Chinese. |
A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer (P19-1)
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| Challenge: | Existing text style transfer methods face three challenges: 1) the transfer is weakly interpretable; 2) generated outputs struggle in content preservation; 3) the trade-off between content and style is intractable. |
| Approach: | They propose a hierarchical reinforced sequence operation method that proposes operation positions and alters the sentence. |
| Outcome: | The proposed method significantly outperforms existing methods on two text style transfer datasets. |
Handling Divergent Reference Texts when Evaluating Table-to-Text Generation (P19-1)
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| Challenge: | Existing text generation metrics rely on reference texts, such as BLEU and ROUGE, but they are too expensive to apply repeatedly. |
| Approach: | They propose a metric which aligns n-grams from the generated texts to the semi-structured data before computing their precision and recall. |
| Outcome: | The proposed metric correlates with human judgments better than existing text generation metrics while being easier to use. |
Unsupervised Question Answering by Cloze Translation (P19-1)
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| Challenge: | Existing QA datasets only available for limited domains and languages. |
| Approach: | They propose to generate context, question and answer triples in an unsupervised manner and synthesize extractive QA training data automatically. |
| Outcome: | The proposed approach outperforms existing QA models on a common EQA benchmark dataset. |
MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension (P19-1)
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| Challenge: | A large number of reading comprehension (RC) datasets have been created, but little research has been done on whether they generalize to one another and the extent to which existing datasets can be leveraged for improving performance on new ones. |
| Approach: | They propose a BERT-based reading comprehension model that can be trained on multiple RC datasets. |
| Outcome: | The proposed model can be trained on multiple RC datasets and improve performance on five RC data. |
Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives (P19-1)
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Yi Tay, Shuohang Wang, Anh Tuan Luu, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, Aston Zhang
| Challenge: | Using a pointer-generator framework for reading/sampling over large documents, we propose a framework for learning over long narratives where documents easily span over thousands of tokens. |
| Approach: | They propose a curriculum learning (CL) based pointer-generator framework for reading/sampling over large documents, enabling diverse training of the neural model based on the notion of alternating contextual difficulty. |
| Outcome: | The proposed framework improves on the NarrativeQA reading comprehension benchmark and reaches state-of-the-art performance. |
Explain Yourself! Leveraging Language Models for Commonsense Reasoning (P19-1)
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| Challenge: | Empirical results indicate that we can effectively leverage language models for commonsense reasoning. |
| Approach: | They propose to use commonsense auto-generated explanations to train language models to generate explanations that can be used during training and inference in a commonsensense Auto-Generated Explanation framework. |
| Outcome: | Empirical results show that the proposed framework improves on the commonsenseQA task by 10%. |
Interpretable Question Answering on Knowledge Bases and Text (P19-1)
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| Challenge: | Existing evaluation paradigms for ML based question answering models are lacking . a lack of explanation methods has been proposed for QA models . |
| Approach: | They propose an automatic evaluation paradigm for explanation methods in ML based question answering models . they adapt post hoc explanation methods such as LIME and input perturbation to the model . |
| Outcome: | The proposed evaluation paradigm compares explanation methods with human annotations. |
A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings Based on Graph Modularity (P19-1)
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| Challenge: | Cross-lingual word embeddings encode the meaning of words from different languages into a shared low-dimensional space. |
| Approach: | They show that modularity can be used to improve unsupervised word embeddings . they show that it is useful for low-resource languages where one often has few bilingual pairs . |
| Outcome: | The proposed model improves unsupervised cross-lingual word embeddings on distant language pairs in low-resource settings. |
Multilingual and Cross-Lingual Graded Lexical Entailment (P19-1)
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| Challenge: | a novel method for capturing graded (and binary) LE is developed for cross-lingual generalisation of lexical entailment . lexicale enlargement is a key principle behind hierarchical structure found in semantic networks . |
| Approach: | They propose a method for cross-lingual generalisation of GR-LE relation using hyperlex and a bilingual dictionary. |
| Outcome: | The proposed method outperforms current state-of-the-art on binary cross-lingual LE detection by a wide margin. |
What Kind of Language Is Hard to Language-Model? (P19-1)
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| Challenge: | a recent study suggests that language models perform poorly across languages. |
| Approach: | They propose a model that fits a paired-sample multiplicative mixed-effects model to obtain language difficulty coefficients from at least-pairwise parallel corpora. |
| Outcome: | The proposed model is able to handle missing data and is aware of inter-sentence variation. |
Analyzing the Limitations of Cross-lingual Word Embedding Mappings (P19-1)
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| Challenge: | Existing methods for cross-lingual word embeddings have limited results . existing methods require little or no cross-linguistic signal to work . |
| Approach: | They compare offline mapping methods to an extension of skip-gram that jointly learns both embedding spaces. |
| Outcome: | The proposed method yields more isomorphic embeddings, is less sensitive to hubness, and achieves stronger results in bilingual lexicon induction. |
How Multilingual is Multilingual BERT? (P19-1)
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| Challenge: | Existing studies have shown that deep, contextualized language models can encode syntactic and named entity information, but they have focused on what models trained on English capture about English. |
| Approach: | They propose a multilingual model pre-trained from monolingual Wikipedia corpora . they show that multilingual BERT is surprisingly good at zero-shot cross-lingual model transfer . |
| Outcome: | The proposed model can find translation pairs, but it exhibits systematic deficiencies affecting certain language pairs. |
Bilingual Lexicon Induction through Unsupervised Machine Translation (P19-1)
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| Challenge: | Existing methods for bilingual lexicon induction use nearest neighbor or related retrieval methods to induce word translation pairs. |
| Approach: | They propose a method that aligns word embeddings in two languages and uses them to build a phrase-table and a language model to extract the bilingual lexicon. |
| Outcome: | The proposed method improves accuracy 6 points over nearest neighbor and 4 points over CSLS retrieval on the same cross-lingual embeddings. |
Automatically Identifying Complaints in Social Media (P19-1)
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| Challenge: | Complaining is a basic speech act used to express a negative mismatch between reality and expectations in a particular situation. |
| Approach: | They present a systematic analysis of complaints in computational linguistics . they collect annotated data set of written complaints expressed on Twitter . |
| Outcome: | The proposed model achieves predictive performance of up to 79 F1 using distant supervision. |
TWEETQA: A Social Media Focused Question Answering Dataset (P19-1)
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Wenhan Xiong, Jiawei Wu, Hong Wang, Vivek Kulkarni, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang
| Challenge: | Social media is becoming an important realtime information source, especially during natural disasters and emergencies. |
| Approach: | They present a large-scale dataset for question answering over social media data . they gather tweets used by journalists and ask human annotators to write questions upon them . |
| Outcome: | The proposed dataset shows that neural models that perform well on formal texts are limited in their performance . the proposed model is still lagging behind human performance with a large margin . |
Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums (P19-1)
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| Challenge: | Existing work on teaching machines to ask questions focused on generating fixed answers. |
| Approach: | They propose a model to generate open-answered questions from real-world news for open discussion . they analyze how language use affects the number of answers . |
| Outcome: | The proposed model generates questions with higher quality than most text generation methods. |
Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations (P19-1)
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| Challenge: | Existing approaches to learn from social-media conversations have been proposed to identify and contain fake news shared on social media platforms. |
| Approach: | They propose to represent social-media conversations as binarized constituency trees that allows comparing features in source-posts and their replies effectively. |
| Outcome: | The proposed models outperform the current best model by 12% and 15% on F1-macro for rumor-veracity classification and stance classification tasks respectively. |
HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization (P19-1)
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| Challenge: | Neural extractive summarization models employ hierarchical encoders with inaccurate sentence-level labels. |
| Approach: | They propose a method to pre-train a hierarchical encoder with unlabeled data. |
| Outcome: | The proposed model outperforms its initialized counterpart by 1.25 ROUGE on CNN and 2.0 ROUGEE on a version of New York Times dataset. |
Hierarchical Transformers for Multi-Document Summarization (P19-1)
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| Challenge: | Existing models for multidocument summarization have been developed that can process multiple documents in a hierarchical manner. |
| Approach: | They propose a neural summarization model which can process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner. |
| Outcome: | The proposed model improves on the WikiSum dataset and can process multiple documents in a hierarchical manner. |
Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization (P19-1)
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| Challenge: | Abstractive text summarization is a demanding, time expensive and generally laborious task. |
| Approach: | They propose a framework for enhancing abstractive text summarization using deep learning techniques and semantic data transformations. |
| Outcome: | The proposed method is evaluated on two popular datasets with encouraging results. |
Studying Summarization Evaluation Metrics in the Appropriate Scoring Range (P19-1)
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| Challenge: | Existing evaluation metrics are compared based on their ability to correlate with humans, but they disagree in the higher-scoring range in which current systems operate. |
| Approach: | They show that evaluation metrics which behave similarly on these datasets strongly disagree in the higher-scoring range in which current systems operate. |
| Outcome: | The evaluation metrics which behave similarly on these datasets strongly disagree in the higher-scoring range in which current systems operate. |
Simple Unsupervised Summarization by Contextual Matching (P19-1)
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| Challenge: | Existing methods for sentence summarization require a large amount of parallel data for supervision to work. |
| Approach: | They propose an unsupervised method for sentence summarization using only language modeling. |
| Outcome: | The proposed method maintains continuous contextual matching while maintaining output fluency without any paired examples. |
Generating Summaries with Topic Templates and Structured Convolutional Decoders (P19-1)
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| Challenge: | Existing neural generation approaches create multi-sentence text as a single sequence . Existing approaches create multiple sentences as if they were a sequence based on content structure . |
| Approach: | They propose a structured convolutional decoder that is guided by the content structure of target summaries. |
| Outcome: | The proposed model outperforms existing decoders on three datasets representing different domains. |
Morphological Irregularity Correlates with Frequency (P19-1)
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| Challenge: | a new measure of irregularity is proposed for inflectional morphology of languages . the measure is based on the predictability of forms in a language . |
| Approach: | They propose a measure of irregularity based on the predictability of forms in a language. |
| Outcome: | The proposed measure is the first of its breadth and confirms longstanding proposals from the linguistics literature. |
Like a Baby: Visually Situated Neural Language Acquisition (P19-1)
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| Challenge: | A multi-modal neural architecture outperforms its equivalent trained on language alone with a 2% decrease in perplexity . |
| Approach: | They propose to use visual context to train neural language models to perform next-word prediction. |
| Outcome: | The proposed model outperforms its equivalent trained on language with 2% decrease in perplexity even when no visual context is available at test. |
Relating Simple Sentence Representations in Deep Neural Networks and the Brain (P19-1)
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| Challenge: | Existing deep learning models for natural language processing are not fully studied. |
| Approach: | They investigate whether deep recurrent models learn sentences against those encoded by the brain and whether there is any correspondence between hidden layers of these models and brain regions when processing sentences. |
| Outcome: | The proposed models can be used to synthesize brain data and improve subsequent stimuli decoding accuracy. |
Modeling Affirmative and Negated Action Processing in the Brain with Lexical and Compositional Semantic Models (P19-1)
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| Challenge: | Existing studies have shown that distributional semantic models can be used to decode fMRI patterns associated with specific aspects of semantic composition, such as the negation function. |
| Approach: | They apply lexical and compositional semantic models to decode fMRI patterns associated with negated and affirmative sentences containing hand-action verbs. |
| Outcome: | The proposed models show reduced decoding of sentences where the verb is in the negated context, as compared to the affirmative one, within brain regions implicated in action-semantic processing. |
Word-order Biases in Deep-agent Emergent Communication (P19-1)
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| Challenge: | a recent study examines the "natural" word-order constraints that constrain neural networks . we train models to communicate about paths in a simple gridworld . |
| Approach: | They propose to inoculate a notion of "effort" into neural networks to make their linguistic behavior more human-like. |
| Outcome: | The proposed models show a strong tendency to avoid redundancy and minimize long-distance dependencies. |
NNE: A Dataset for Nested Named Entity Recognition in English Newswire (P19-1)
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| Challenge: | Named entity recognition (NER) is widely used in downstream tasks but most tools focus on flat mention structure over coarse schemas. |
| Approach: | They describe a fine-grained, nested named entity dataset over the Wall Street Journal portion of the Penn Treebank. |
| Outcome: | The proposed dataset comprises 279,795 mentions of 114 entity types with up to 6 layers of nesting. |
Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks (P19-1)
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| Challenge: | Named entity recognition (NER) approaches restrict each word belonging to at most one entity mention. |
| Approach: | They propose to model and leverage the head-driven phrase structures of entity mentions to solve this problem. |
| Outcome: | The proposed architecture achieves state-of-the-art on three standard nested entity mention detection benchmarks. |
Improving Textual Network Embedding with Global Attention via Optimal Transport (P19-1)
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Liqun Chen, Guoyin Wang, Chenyang Tao, Dinghan Shen, Pengyu Cheng, Xinyuan Zhang, Wenlin Wang, Yizhe Zhang, Lawrence Carin
| Challenge: | Existing methods for learning textual network embeddings are noisy and sparse. |
| Approach: | They propose to use text-based attention parsing to learn context-aware network embeddings. |
| Outcome: | The proposed model outperforms state-of-the-art methods in a number of domains. |
Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction (P19-1)
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| Challenge: | Recent years have witnessed a significant increase in laboratory-based evaluation benchmarks in many scientific disciplines. |
| Approach: | They propose to use NLP datasets to extract task, dataset, metric and score from NLP papers to build automatic leaderboards. |
| Outcome: | The proposed model outperforms baselines in the NLP domain by a large margin. |
Scaling up Open Tagging from Tens to Thousands: Comprehension Empowered Attribute Value Extraction from Product Title (P19-1)
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| Challenge: | Existing models treat each attribute as an entity type and build one set of NER tags for each of them, leading to scalability issues. |
| Approach: | They propose to regard attribute as a query and adopt only one global set of BIO tags for any attributes to reduce the burden of attribute tag or model explosion. |
| Outcome: | The proposed model outperforms state-of-the-art models and generates promising results for 8,906 attributes. |
Incorporating Linguistic Constraints into Keyphrase Generation (P19-1)
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| Challenge: | Existing keyphrase generation methods generate overlapping phrases (including sub-phrases or super-phrase) Existing methods are far from satisfactory for a wide range of natural language processing tasks. |
| Approach: | They propose a parallel Seq2Seq network with coverage attention to alleviate the overlapping phrase problem by integrating linguistic constraints of keyphrase into the basic Seq2-Sequeq network on the source side and employ the multi-task learning framework on the target side. |
| Outcome: | The proposed method outperforms the state-of-the-art CopyRNN on scientific datasets and is also more effective in news domain. |
A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining (P19-1)
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| Challenge: | Existing methods of identifying ADRs are reliable but time-consuming and offer a limited amount of ADR relevant information. |
| Approach: | They propose a neural network-inspired multi-task learning framework that can simultaneously extract ADRs from various sources. |
| Outcome: | The proposed framework achieves state-of-the-art performance on three publicly available real-world benchmark pharmacovigilance datasets, a Twitter dataset from PSB 2016 Social Me- dia Shared Task, CADEC corpus and Medline ADR corpus. |
Quantity Tagger: A Latent-Variable Sequence Labeling Approach to Solving Addition-Subtraction Word Problems (P19-1)
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| Challenge: | Existing methods to solve arithmetic word problems require additional annotations. |
| Approach: | They propose a method that automatically discovers hidden mathematical relations by tagging each quantity with a sign corresponding to one type of mathematical operation. |
| Outcome: | Empirical results show that the proposed method achieves 5 and 8 points of accuracy gains on two datasets compared to prior approaches. |
A Deep Reinforced Sequence-to-Set Model for Multi-Label Classification (P19-1)
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| Challenge: | Multi-label classification (MLC) aims to assign multiple labels to each sample. |
| Approach: | They propose a sequence-to-set model that is trained via reinforcement learning and rewards feedback independent of the label order. |
| Outcome: | The proposed model outperforms baseline models and reduces sensitivity to label order. |
Joint Slot Filling and Intent Detection via Capsule Neural Networks (P19-1)
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| Challenge: | Existing models that label slots and detect intent do not preserve hierarchical relationship between words, slots, and intents. |
| Approach: | They propose a capsule-based neural network model which performs slot filling and intent detection via a dynamic routing-by-agreement schema. |
| Outcome: | The proposed model performs better than existing models and existing models on real-world datasets. |
Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision (P19-1)
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| Challenge: | Lack of labeled training data is a major bottleneck for aspect and opinion term extraction . et al., 2004: aspect and opinions are of particular importance for opinion mining . |
| Approach: | They propose to automatically mine extraction rules from existing training examples based on dependency parsing results . they then apply the mined rules to label auxiliary data to train a neural model . |
| Outcome: | The proposed algorithm can learn from human annotated data and human annnotated auxiliary data. |
Cost-sensitive Regularization for Label Confusion-aware Event Detection (P19-1)
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| Challenge: | Recent advances in event detection focus on wordwise classification with one NIL class for tokens do not trigger any event. |
| Approach: | They propose a cost-sensitive regularization method which penalizes more on mislabeling . they propose two estimators which can effectively measure such label confusion based on instance-level statistics . |
| Outcome: | The proposed method can improve the performance of different models in English and Chinese event detection. |
Exploring Pre-trained Language Models for Event Extraction and Generation (P19-1)
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| Challenge: | Existing methods to extract event data are laborious to create and limited in size. |
| Approach: | They propose an event extraction model to overcome the roles overlap problem by separating the argument prediction in terms of roles. |
| Outcome: | The proposed method surpasses existing methods on the ACE2005 dataset and improves on the previous methods. |
Improving Open Information Extraction via Iterative Rank-Aware Learning (P19-1)
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| Challenge: | Open information extraction (IE) is the task of extracting open-domain assertions from natural language sentences. |
| Approach: | They propose an additional binary classification loss to calibrate the extraction likelihood . they propose an iterative learning process where extractions generated by the open IE model are incrementally included as training samples to help the model learn from trial and error. |
| Outcome: | Experiments on open information extraction (IE) show that the extraction likelihood is not well calibrated when comparing quality of extracted assertions. |
Towards Improving Neural Named Entity Recognition with Gazetteers (P19-1)
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| Challenge: | Currently, neural models for named entity recognition are based on data-driven models, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features. |
| Approach: | They propose to use external gazetteers to efficiently access annotated data to generalize beyond the annotation of entities. |
| Outcome: | The proposed model can access external gazetteers while avoiding the effort to design hand-crafted features. |
Span-Level Model for Relation Extraction (P19-1)
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| Challenge: | Recent approaches for this span-level task have inherent limitations. |
| Approach: | They propose a model which directly models all possible spans and performs joint entity mention detection and relation extraction. |
| Outcome: | The proposed model performs joint entity mention detection and relation extraction on the ACE2005 dataset. |
Enhancing Unsupervised Generative Dependency Parser with Contextual Information (P19-1)
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| Challenge: | Existing approaches to unsupervised dependency parsing are based on probabilistic generative models that learn the joint distribution of the given sentence and its parse. |
| Approach: | They propose a probabilistic model that generates a sentence and its parse from a latent representation, which encodes global contextual information of the generated sentence. |
| Outcome: | The proposed model achieves competitive accuracy compared with state-of-the-art models. |
Neural Architectures for Nested NER through Linearization (P19-1)
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| Challenge: | a nested named entity recognition (NER) is a set of entities that can overlap and be labeled with more than one label. |
| Approach: | They propose two neural network architectures for nested named entity recognition . they propose to model nesting entities as multilabels and predict a sequence-to-sequence problem . |
| Outcome: | The proposed methods outperform the state-of-the-art on four corpora . the proposed models also improve on the recently published contextual embeddings . |
Online Infix Probability Computation for Probabilistic Finite Automata (P19-1)
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| Challenge: | Probabilistic finite automata (PFAs) are statistical language models used in natural language processing. |
| Approach: | They develop an asymptotic algorithm to compute the infix probabilities of each prefix of a string from streaming data. |
| Outcome: | The proposed algorithm improves the infix probabilities of a weighted automata from streaming data. |
How to Best Use Syntax in Semantic Role Labelling (P19-1)
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| Challenge: | Existing studies on integrating external information into NLP tasks focus on word-level shallow features such as POS or chunk tags. |
| Approach: | They propose to integrate syntactic information into a neural ELMo-based SRL sequence labelling model by using a constituency representation as input features. |
| Outcome: | The proposed approach improves performance on the in-domain CoNLL’05 and CoNll’12 benchmarks. |
PTB Graph Parsing with Tree Approximation (P19-1)
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| Challenge: | a new method for nonlocal dependency identification is proposed for the Penn Treebank (PTB) syntactic structures are represented as graphs due to nonlocal dependencies . |
| Approach: | They propose a method that approximates PTB graph-structured representations by trees . their method reduces nonlocal dependency identification and constituency parsing into single parser . |
| Outcome: | The proposed method outperforms the previous methods in nonlocal dependency identification. |
Sequence Labeling Parsing by Learning across Representations (P19-1)
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| Challenge: | Constituency and dependency parsing are the main abstractions for representing syntactic structure of sentences . constituency parsers are considered disjointed tasks, and their improvements have been obtained separately. |
| Approach: | They propose to add auxiliary loss to constituency parsing paradigms and explore a model that parses both paradigms at no cost. |
| Outcome: | The proposed model outperforms single-task models by 1.05 F1 points and 0.62 UAS points for constituency parsing and dependency parsers. |
A Prism Module for Semantic Disentanglement in Name Entity Recognition (P19-1)
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| Challenge: | Xu et al., 2015) proposed a noise reduction mechanism to disentangle semantics of words . hard and soft attention mechanisms are used to reduce noise in NLP tasks . |
| Approach: | They propose a prism module to disentangle semantic aspects of words and reduce noise . they propose combining prism modules with downstream models to improve model performance . |
| Outcome: | The proposed method significantly improves the performance of baselines on named entity recognition (NER) tasks. |
Label-Agnostic Sequence Labeling by Copying Nearest Neighbors (P19-1)
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| Challenge: | Retrieve-and-edit based structured prediction models condition on retrieved neighbors to generate new structures, but many models do not explicitly capture the discrete operations that allow for the neighbors to be edited into the target structure. |
| Approach: | They propose to explicitly condition on retrieved neighbors to create new structures . they propose to use a dynamic programming approach to sequence labeling . |
| Outcome: | The proposed model can perform accurate sequence labeling by explicitly copying labels from retrieved neighbors. |
Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset (P19-1)
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| Challenge: | EmpatheticDialogues dataset provides a benchmark for empathetic dialogue generation . human evaluators perceive dialogue models as more epathetic . |
| Approach: | They propose a benchmark for empathetic dialogue generation from a dataset of 25k conversations grounded in emotional situations. |
| Outcome: | The proposed benchmarks show that existing models are perceived to be more empathetic by human evaluators compared to models trained on large-scale Internet conversations. |
Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment (P19-1)
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| Challenge: | Existing approaches to generate informative responses based on external knowledge are limited to singleround settings. |
| Approach: | They propose a framework for multi-turn conversations with two dialogue agents . they propose to evaluate dialogues on informativeness and coherence . |
| Outcome: | The proposed framework outperforms state-of-the-art approaches significantly on the publicly available dataset. |
Training Neural Response Selection for Task-Oriented Dialogue Systems (P19-1)
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Matthew Henderson, Ivan Vulić, Daniela Gerz, Iñigo Casanueva, Paweł Budzianowski, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, Pei-Hao Su
| Challenge: | Despite their popularity, retrieval-based models have had modest impact on task-oriented dialogue systems . main obstacle to their application is the low-data regime of most task-orientated dialogue tasks . e-commerce, banking, and other domains are applications of retrieval models . |
| Approach: | They propose a method which pretrains a retrieval-based model on large general-domain conversational corpora and fine-tunes it for the target dialogue domain. |
| Outcome: | The proposed method is evaluated on five diverse domains, ranging from e-commerce to banking. |
Collaborative Dialogue in Minecraft (P19-1)
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| Challenge: | Using computer games to simulate grounded situations, we want to develop interactive agents that can communicate with humans to solve tasks in grounded scenarios. |
| Approach: | They propose a Minecraft-based collaborative building task in which one player is shown a building structure and needs to instruct the other player to build it. |
| Outcome: | The proposed agent can communicate with humans to solve a building task in a Minecraft-based environment without the need for physical robots. |
Neural Response Generation with Meta-words (P19-1)
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| Challenge: | Experimental results show that meta-words can be used to generate open domain dialogues . human-machine conversation is a fundamental problem in NLP . |
| Approach: | They propose a goal-tracking memory network that formalizes meta-word expression as a target in response generation and manages the generation process with a state memory panel and a controller. |
| Outcome: | The proposed model outperforms state-of-the-art generation models in response relevance, response diversity, and accuracy. |
Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading (P19-1)
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Lianhui Qin, Michel Galley, Chris Brockett, Xiaodong Liu, Xiang Gao, Bill Dolan, Yejin Choi, Jianfeng Gao
| Challenge: | a new approach to contentful neural conversation is proposed . end-to-end models are effective in learning fluent responses, but their responses are often vacuous and uninformative. |
| Approach: | They propose a model that provides the conversation model with relevant text on the fly as a source of external knowledge. |
| Outcome: | The proposed model improves the informativeness and diversity of generated output compared to previous methods. |
Ordinal and Attribute Aware Response Generation in a Multimodal Dialogue System (P19-1)
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| Challenge: | Existing multimodal dialogue systems are based on unimodal sources, capturing information from text and image. |
| Approach: | They propose a position and attribute aware attention mechanism to learn enhanced image representation conditioned on the user utterance. |
| Outcome: | The proposed model outperforms the state-of-the-art models on text similarity metrics. |
Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference (P19-1)
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| Challenge: | Existing models for SLU use explicit memory representations, but the context memory is under-exploited. |
| Approach: | They propose a dialogue logistic inference task to consolidate the context memory with SLU in a multi-task framework. |
| Outcome: | The proposed model improves slot filling and domain classification performance in a multi-task framework. |
Personalizing Dialogue Agents via Meta-Learning (P19-1)
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| Challenge: | Existing personalized dialogue models use human designed persona descriptions to improve dialogue consistency. |
| Approach: | They propose to extend Model-Agnostic Meta-Learning (MAML) to personalized dialogue learning without using persona descriptions. |
| Outcome: | The proposed model outperforms baseline models in terms of human-evaluated fluency and consistency on a persona-chat dataset. |
Reading Turn by Turn: Hierarchical Attention Architecture for Spoken Dialogue Comprehension (P19-1)
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| Challenge: | Existing research on multi-turn spoken conversations focuses on reading comprehension of passages . interactivity of spoken content can cause lower information density and topic diffusion . |
| Approach: | They propose a hierarchical attention neural network architecture to improve spoken dialogue comprehension by combining turn-level and word-level attention mechanisms. |
| Outcome: | The proposed approach outperforms baseline attention models and is robust to lengthy and out-of-distribution test samples. |
A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling (P19-1)
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| Challenge: | Existing models for slot filling and intent detection lack bi-directional interrelated connections between the intent and slots. |
| Approach: | They propose a bi-directional interrelated model for slot filling and intent detection that uses an SF-ID network to establish direct connections between the two tasks to promote each other mutually. |
| Outcome: | The proposed model improves on ATIS and Snips datasets in sentence-level semantic frame accuracy and improves performance on the two tasks. |
Dual Supervised Learning for Natural Language Understanding and Generation (P19-1)
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| Challenge: | Natural language understanding and natural language generation are important research topics in the NLP and dialogue fields. |
| Approach: | They propose a dual-supervised learning framework for natural language understanding and generation on top of dual supervised learning. |
| Outcome: | The proposed framework boosts the performance of both tasks simultaneously in the benchmark experiments. |
SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking (P19-1)
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| Challenge: | Existing methods to model domain- and slot-dependent belief trackers have difficulty adding new slot-values, resulting in lack of flexibility of domain ontology configurations. |
| Approach: | They propose a model that captures relationships between domain-slot-types and slot-values appearing in utterances through attention mechanisms based on contextual semantic vectors. |
| Outcome: | The proposed model improves performance on two dialog corpora and achieves state-of-the-art accuracy. |
Robust Zero-Shot Cross-Domain Slot Filling with Example Values (P19-1)
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| Challenge: | Task-oriented dialog systems rely on deep learning-based slot filling models . little to no training data for target domains may be available or schemas may not be aligned . |
| Approach: | They propose to use slot descriptions and examples of slot values to learn slot semantic representations that are transferable across domains and robust to misaligned schemas. |
| Outcome: | The proposed model outperforms state-of-the-art models on two multi-domain datasets on low-data setting. |
Deep Unknown Intent Detection with Margin Loss (P19-1)
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| Challenge: | Existing methods for detecting unknown intents are difficult due to lack of examples. |
| Approach: | They propose a method for detecting unknown intents using bidirectional long-term memory networks with the margin loss as the feature extractor. |
| Outcome: | The proposed method can yield consistent improvements on two benchmark datasets. |
Modeling Semantic Relationship in Multi-turn Conversations with Hierarchical Latent Variables (P19-1)
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| Challenge: | Existing work on multi-turn conversations has focused on the relationship between the response and context, but it is lacking a model to model the relationship. |
| Approach: | They propose a conversational semantic relationship RNN model to construct hierarchical dependency between utterances and their context. |
| Outcome: | The proposed model significantly improves the quality of responses in terms of fluency, coherence, and diversity compared to baseline methods. |
Rationally Reappraising ATIS-based Dialogue Systems (P19-1)
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| Challenge: | Recent state-of-the-art neural models have obtained F1-scores near 98% on the task of slot filling. |
| Approach: | They propose to fix annotation errors in ATIS and propose a rule-based grammar for slot filling that achieves a 95.82% F1 score. |
| Outcome: | The proposed grammar achieves a 95.82% F1-score on the ATIS domain. |
Learning Latent Trees with Stochastic Perturbations and Differentiable Dynamic Programming (P19-1)
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| Challenge: | Discrete structures are ubiquitous in the study of natural languages, for example in morphology, syntax and discourse analysis. |
| Approach: | They treat projective dependency trees as latent variables in their probabilistic model and induce them in such a way as to be beneficial for a downstream task. |
| Outcome: | The proposed model is effective on sentiment analysis and natural language inference tasks and is fully differentiable. |
Neural-based Chinese Idiom Recommendation for Enhancing Elegance in Essay Writing (P19-1)
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| Challenge: | idiom recommendation is difficult because remembering idiomatic expressions is difficult, authors say . idiomas are often written in ancient classical Chinese for conciseness, meaning is difficult to remember . |
| Approach: | They propose to use a neural machine translation framework to recommend idioms . they assume that idiomatic expressions are written with one pseudo target language . |
| Outcome: | The proposed approach achieves promising performance compared with baseline methods. |
Better Exploiting Latent Variables in Text Modeling (P19-1)
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| Challenge: | Consistent gains in performance on two datasets, Penn Treebank and Yahoo, indicate the generalizability of our method. |
| Approach: | They propose a method to exploit latent variables through hidden state averaging by sampling latent variable multiple times at a gradient step. |
| Outcome: | The proposed method shows consistent gains on two datasets showing that it is generalizable. |
Misleading Failures of Partial-input Baselines (P19-1)
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| Challenge: | Recent work establishes dataset difficulty and removes annotation artifacts via partial-input baselines. |
| Approach: | They propose to use partial-input baselines to establish dataset difficulty . they show how trivial patterns only visible in the full input can evade partial-output baseline . |
| Outcome: | The proposed model can solve 15% of previously-thought "hard" examples. |
Soft Contextual Data Augmentation for Neural Machine Translation (P19-1)
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| Challenge: | Existing methods for enhancing training data are limited in natural language tasks due to text characteristics. |
| Approach: | They propose a data augmentation method that softly augments a randomly chosen word in a sentence by its contextual mixture of multiple related words. |
| Outcome: | The proposed method outperforms baseline methods on small and large scale machine translation datasets. |
Reversing Gradients in Adversarial Domain Adaptation for Question Deduplication and Textual Entailment Tasks (P19-1)
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| Challenge: | Existing domain adaptation techniques for question deduplication and RTE focus on transferring category independent knowledge between domains. |
| Approach: | They propose to use gradient reversal to explicitly learn shared and unshared (domain specific) representations between two textual domains to compensate for domain mismatch while distilling domain specific knowledge. |
| Outcome: | The proposed approach outperforms other methods on question deduplication and on recognizing textual entailment tasks, while still distilling domain specific knowledge. |
Towards Integration of Statistical Hypothesis Tests into Deep Neural Networks (P19-1)
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| Challenge: | Existing approaches for text classification are lexicallevel features with Naive Bayes or Support Vector Machines (SVM) . |
| Approach: | They propose a deep-learning model that uses label descriptions to train texts and their labels for multi-label and multi-class classification tasks. |
| Outcome: | The proposed model improves on one set with a high margin and on all other sets with competitive results. |
Depth Growing for Neural Machine Translation (P19-1)
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| Challenge: | Neural machine translation models with tens and even more than a hundred blocks have shown effectiveness in image recognition. |
| Approach: | They propose a two-stage approach with three specially designed components to construct deeper NMT models. |
| Outcome: | The proposed approach improves on WMT14 EnglishGerman and EnglishFrench translation tasks. |
Generating Fluent Adversarial Examples for Natural Languages (P19-1)
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| Challenge: | Current methods for building adversarial attackers for NLP are inefficient as the gradient is discarded. |
| Approach: | They propose an adversarial attacker which performs Metropolis-Hastings sampling with the guidance of gradients to solve these problems. |
| Outcome: | The proposed algorithm outperforms the baseline model on attacking capability on IMDB and SNLI. |
Towards Explainable NLP: A Generative Explanation Framework for Text Classification (P19-1)
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| Challenge: | Existing approaches for explainable machine learning systems focus on interpreting outputs or connections between inputs and outputs. |
| Approach: | They propose a generative explanation framework that learns to make classification decisions and generates fine-grained explanations at the same time. |
| Outcome: | The proposed framework surpasses all baselines on two datasets and generates concise explanations at the same time. |
Combating Adversarial Misspellings with Robust Word Recognition (P19-1)
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| Challenge: | a sub-field of word recognition models is emerging to combat adversarial spelling mistakes . imperceptible attacks can cause models to misclassify examples, but training robust models remains a challenge . |
| Approach: | They propose to place a word recognition model in front of a downstream classifier to combat adversarial spelling mistakes. |
| Outcome: | The proposed model outperforms adversarial training and off-the-shelf spell checkers in a word recognition task. |
An Empirical Investigation of Structured Output Modeling for Graph-based Neural Dependency Parsing (P19-1)
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| Challenge: | In the past few years, graph-based dependency parsers have led to impressive empirical successes on parsing accuracy. |
| Approach: | They propose to use a graph-based dependency parser to model global outputs. |
| Outcome: | The proposed model has been shown to perform better on sentence-level Complete Match metric compared with the previous model. |
Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes (P19-1)
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| Challenge: | a new study examines the role of dialogue observers in psychotherapy . the model is based on motivational interviewing, which is effective for treating addictions . |
| Approach: | They propose to model MI behavioral codes for therapists by an observer . they propose to use the observer to forecast therapist and client MI behavioral code . |
| Outcome: | The proposed model outperforms baseline models for both tasks and reveals tradeoffs in performance. |
Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems (P19-1)
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| Challenge: | Existing work on video-grounded dialogue systems is limited by feature space and semantic information. |
| Approach: | They propose multimodal transformer networks to encode videos and incorporate information from different modalities. |
| Outcome: | The proposed system generates appropriate conversational response to queries of humans based on visual and audio aspects of a given video . it also generalizes to another multimodal visual-grounded dialogue task, and obtains promising performance. |
Target-Guided Open-Domain Conversation (P19-1)
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| Challenge: | a new study aims to improve opendomain chat systems by integrating goals and strategy into the system. |
| Approach: | They propose a structured approach that introduces coarse-grained keywords to control intended content of system responses and attains smooth conversation transition through turn-level supervised learning. |
| Outcome: | The proposed system produces meaningful and effective conversations significantly better than other approaches. |
Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good (P19-1)
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| Challenge: | Persuasion agents are a form of communication that can be used to change people's opinions and actions for social good. |
| Approach: | They designed an online persuasion task where one participant was asked to persult the other to donate to a specific charity. |
| Outcome: | The proposed system could change people's opinions and actions for social good. |
Improving Neural Conversational Models with Entropy-Based Data Filtering (P19-1)
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| Challenge: | Current neural network-based conversational models lack diversity and generate boring responses to open-ended utterances. |
| Approach: | They propose an unsupervised method of filtering dialog datasets by removing generic utterances from training data using an entropy-based approach that does not require human supervision. |
| Outcome: | The proposed method improves dialog quality as chatbots learn to output more diverse responses to open-ended utterances. |
Zero-shot Word Sense Disambiguation using Sense Definition Embeddings (P19-1)
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| Challenge: | Word Sense Disambiguation (WSD) is an open problem in Natural Language Processing . current methods treat senses as discrete labels and predict the most-frequent-Sense for unseen senses . |
| Approach: | They propose a supervised model to perform Word Sense Disambiguation (WSD) by predicting over a continuous sense embedding space rather than a discrete label space. |
| Outcome: | The proposed model generalizes over seen and unseen senses, achieving zero-shot learning. |
Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation (P19-1)
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| Challenge: | Contextual embeddings address the problem of meaning conflation hampering word embeddables. |
| Approach: | They propose a method that creates sense-level embeddings with full-coverage of WordNet without recourse to explicit sense distributions or task-specific modelling. |
| Outcome: | The proposed method surpasses previous systems using powerful models and is robust when ignoring part-of-speech and lemma features. |
Word2Sense: Sparse Interpretable Word Embeddings (P19-1)
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| Challenge: | Word2Sense embeddings are interpretable, but they are sparse and fast to compute . a unitary rotation can be applied to many of these embeddables retaining their utility for computational tasks while changing the values of individual coordinates. |
| Approach: | They propose an unsupervised method to generate Word2Sense word embeddings that are interpretable. |
| Outcome: | The proposed method compares well with other unsupervised word embeddings on NLP tasks. |
Modeling Semantic Compositionality with Sememe Knowledge (P19-1)
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| Challenge: | Semantic compositionality (SC) is defined as the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents. |
| Approach: | They propose to incorporate sememes into SC models and employ them in learning multiword expressions. |
| Outcome: | The proposed models achieve significant performance boost compared to baseline methods without sememe knowledge. |
Predicting Humorousness and Metaphor Novelty with Gaussian Process Preference Learning (P19-1)
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| Challenge: | Inability to quantify key aspects of creative language is a frequent obstacle to natural language understanding. |
| Approach: | They propose a Bayesian approach for predicting humorousness and metaphor novelty using Gaussian process preference learning (GPPL) . |
| Outcome: | The proposed approach achieves a Spearman’s of 0.56 against gold using word embeddings and linguistic features. |
Empirical Linguistic Study of Sentence Embeddings (P19-1)
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| Challenge: | a new method of analysing sentence embeddings shows that linguistic information is retained in the vector representations of sentences. |
| Approach: | They propose a method of analysing the content of sentence embeddings based on probing tasks and contrasting languages. |
| Outcome: | The proposed method is based on probing tasks and classification datasets for two contrasting languages. |
Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings (P19-1)
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| Challenge: | Empirical analysis of word embeddings of ambiguous words is limited by the small size of manually annotated resources and by the fact that word senses are treated as unrelated individual concepts. |
| Approach: | They present a large dataset based on manual Wikipedia annotations and word senses, where word sense from different words are related by semantic classes. |
| Outcome: | The proposed method can predict whether a word is single-sense or multi-sensor, if the sense is frequent, and it can predict rare senses. |
Deep Neural Model Inspection and Comparison via Functional Neuron Pathways (P19-1)
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| Challenge: | a general method for the interpretation and comparison of neural models is proposed . we factor a complex neural model into its functional components . |
| Approach: | They propose a method that factored a complex neural model into its functional components . they use correlated task level and linguistic heuristics to identify correlated pathways . |
| Outcome: | The proposed method can be applied in a purely post-processing manner to understand neural models. |
Collocation Classification with Unsupervised Relation Vectors (P19-1)
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| Challenge: | Existing methods for relation classification are based on word embeddings, but they pose a greater challenge than syntactic and semantic relations. |
| Approach: | They propose a distributional landscape based on word embeddings as a suitable basis for relation classification of collocations . they also conduct experiments on a subset of this benchmark . |
| Outcome: | The proposed dataset is compared to the well known DiffVec dataset and shows that it is more efficient than the standard methods. |
Corpus-based Check-up for Thesaurus (P19-1)
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| Challenge: | Existing thesaurus descriptions are expensive and time-consuming, but there are ways to maintain and improve them. |
| Approach: | They propose to apply a checking procedure to existing thesaurus to find errors . they found errors in word sense descriptions, including inaccurate relationships . |
| Outcome: | The proposed method can reveal errors in thesaurus descriptions, but it is much harder to detect them than with manual methods. |
Confusionset-guided Pointer Networks for Chinese Spelling Check (P19-1)
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| Challenge: | Existing methods to detect and fix errors in Chinese are limited due to context. |
| Approach: | They propose a Confusionset-guided pointer network for Chinese Spell Check task . they propose to use off-the-shelf confusionset to guide character generation . |
| Outcome: | The proposed model outperforms all competitor models on three human-annotated datasets. |
Generalized Data Augmentation for Low-Resource Translation (P19-1)
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| Challenge: | Low-resource language pairs with a lack of parallel data pose challenges for machine translation . data augmentation using monolingual data is an effective way to alleviate the problem . |
| Approach: | They propose a general framework for data augmentation for low-resource machine translation using monolingual data and a related high-resourced language. |
| Outcome: | The proposed method improves translation quality by 1.5 to 8 BLEU points under extreme low-resource settings compared to baselines. |
Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned (P19-1)
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| Challenge: | et al., 2017) show that multi-head attention is important for neural machine translation. |
| Approach: | They evaluate the contribution made by individual attention heads to the overall performance of the Transformer model and analyze the roles played by them in the encoder. |
| Outcome: | The proposed pruning method removes the vast majority of heads without affecting performance. |
Better OOV Translation with Bilingual Terminology Mining (P19-1)
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| Challenge: | Unseen words, also called out-of-vocabulary words, are difficult for machine translation . byte-pair encoding can be used to represent OOVs, but they are often incorrectly translated . |
| Approach: | They propose to use monolingual data to improve the translation of unseen words . they use five target language words to mine target-language sentences . |
| Outcome: | The proposed system can be used to improve translation of out-of-vocabulary words (OOVs) the proposed system is trained on Europarl and can be fine-tuned to improve the translation quality. |
Simultaneous Translation with Flexible Policy via Restricted Imitation Learning (P19-1)
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| Challenge: | Existing approaches to simultaneous translation have been limited and use fixed-latency policies or a complicated two-staged model. |
| Approach: | They propose a single model that adds a “delay” token to the target vocabulary and a restricted dynamic oracle to greatly simplify training. |
| Outcome: | The proposed model achieves better BLEU scores and lower latencies compared to fixed and RL-learned policies on Chinese -> English simultaneous translation. |
Target Conditioned Sampling: Optimizing Data Selection for Multilingual Neural Machine Translation (P19-1)
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| Challenge: | Existing studies show that training on a single related language is more effective than using all data. |
| Approach: | They propose an efficient algorithm that first samples a target sentence, and then conditionally samples its source sentence. |
| Outcome: | The proposed algorithm brings significant gains on three of four languages with minimal training overhead. |
Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Records (P19-1)
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| Challenge: | De-identification is the task of detecting protected health information (PHI) in medical text. |
| Approach: | They propose to create shareable representations of medical text that contain no PHI and can be shared between organizations to create unified datasets for training de-identification models. |
| Outcome: | The proposed representation allows training a simple LSTM-CRF model to an F1 score of 97.4%. |
Merge and Label: A Novel Neural Network Architecture for Nested NER (P19-1)
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| Challenge: | Named entity recognition (NER) is one of the best studied tasks in natural language processing. |
| Approach: | They propose a neural network architecture that merges tokens and/or entities into nested entities and labels them independently. |
| Outcome: | The proposed approach achieves state-of-the-art F1 of 74.6 and improves with contextual embeddings to 82.4. |
Low-resource Deep Entity Resolution with Transfer and Active Learning (P19-1)
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| Challenge: | Entity resolution (ER) is the task of identifying different representations of the same real-world entities across databases. |
| Approach: | They propose a deep learning-based method that targets low-resource settings for ER by combining transfer learning and active learning. |
| Outcome: | The proposed method achieves comparable, if not better, performance compared to state-of-the-art learning-based methods while using an order of magnitude fewer labels. |
A Semi-Markov Structured Support Vector Machine Model for High-Precision Named Entity Recognition (P19-1)
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| Challenge: | Named entity recognition (NER) is the backbone of many NLP solutions. |
| Approach: | They propose a neural semi-Markov structured support vector machine model that controls the precision-recall trade-off by assigning weights to different types of errors in the loss-augmented inference during training. |
| Outcome: | The proposed model achieves better precision-recall trade-off at various precision levels. |
Using Human Attention to Extract Keyphrase from Microblog Post (P19-1)
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| Challenge: | Existing studies on keyphrase extraction neglect human reading behavior during keyphrase annotating. |
| Approach: | They propose to integrate human attention into keyphrase extraction models by an attention mechanism and combine it with neural network models. |
| Outcome: | The proposed models improve on two Twitter datasets. |
Model-Agnostic Meta-Learning for Relation Classification with Limited Supervision (P19-1)
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| Challenge: | Existing methods for relation classification are based on labelled training data, which can be expensive to obtain and available only in limited quantities. |
| Approach: | They propose a model-agnostic meta-learning protocol for training relation classifiers to achieve enhanced predictive performance in limited supervision settings. |
| Outcome: | The proposed protocol improves the performance of two state-of-the-art supervised relation classification models on two relation classification datasets. |
Variational Pretraining for Semi-supervised Text Classification (P19-1)
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| Challenge: | Empirically, we show the relative strength of VAMPIRE against computationally expensive contextual embeddings and other popular semi-supervised baselines under low resource settings. |
| Approach: | They propose a lightweight framework for effective text classification when data and computing resources are limited. |
| Outcome: | The proposed framework is compared with expensive contextual embeddings and semi-supervised baselines under low resource settings. |
Task Refinement Learning for Improved Accuracy and Stability of Unsupervised Domain Adaptation (P19-1)
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| Challenge: | Existing approaches to domain adaptation (DA) require labeled data that can be found in only a handful of domains. |
| Approach: | They propose a task-refinement learning approach to solve pivot detection problems . they propose to train PBLM models with gradually increasing information exposed about each pivot . |
| Outcome: | The proposed approach achieves state-of-the-art accuracy in six domain adaptation setups for sentiment classification. |
Optimal Transport-based Alignment of Learned Character Representations for String Similarity (P19-1)
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| Challenge: | String similarity models are crucial for record linkage, data integration, search and entity resolution systems. |
| Approach: | They propose a model that encodes the characters of each string, aligns the encodings using Sinkhorn Iteration and scores the alignment with a convolutional neural network. |
| Outcome: | The proposed model outperforms state-of-the-art and classical similarity models on four of the five datasets and improves performance by applying it to cross-document coreference. |
The Referential Reader: A Recurrent Entity Network for Anaphora Resolution (P19-1)
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| Challenge: | Existing methods for storing and accessing entity mentions are expensive and implausible for human readers. |
| Approach: | They propose a method for storing and accessing entity mentions during online text processing. |
| Outcome: | The proposed model performs well on a dataset of pronoun-name anaphora. |
Interpolated Spectral NGram Language Models (P19-1)
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| Challenge: | Spectral methods for learning weighted non-deterministic automata have nice theoretical and algorithmic properties, but they have been challenging to get competitive results on language modeling tasks. |
| Approach: | They propose a method for scaling up spectral learning and use interpolated predictions that are optimized to maximize perplexity. |
| Outcome: | The proposed method matches state-of-the-art ngram models while being very fast to train. |
BAM! Born-Again Multi-Task Networks for Natural Language Understanding (P19-1)
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| Challenge: | Existing methods to train multi-task neural networks outperform or even match their single-task counterparts are difficult to implement. |
| Approach: | They propose a method that uses knowledge distillation to train multi-task neural networks that outperform or even match their single-task counterparts. |
| Outcome: | The proposed method outperforms or matches single-task neural networks on the GLUE benchmark. |
Curate and Generate: A Corpus and Method for Joint Control of Semantics and Style in Neural NLG (P19-1)
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| Challenge: | Neural natural language generation (NNLG) models generate syntactically correct utterances from structured inputs without needing hand-crafted rules or templates. |
| Approach: | They propose a method for generating a corpus of parallel meaning representations with rich style markup using freely available and naturally descriptive user reviews. |
| Outcome: | The proposed method can be scalably reused to generate NLG datasets for other domains. |
Automated Chess Commentator Powered by Neural Chess Engine (P19-1)
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| Challenge: | Existing approaches to generate chess commentary are limited in template variety and are not precise enough. |
| Approach: | They propose a neural chess engine into text generation models to help with encoding boards, predicting moves, and analyzing situations. |
| Outcome: | The proposed model can be trained to generate chess commentary texts in 5 categories . the results are both automatic and human evaluations of the model . |
Barack’s Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling (P19-1)
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| Challenge: | Existing language models are only capable of remembering facts seen at training time, and have difficulty recalling them. |
| Approach: | They introduce a knowledge graph language model with mechanisms for selecting and copying facts from a Knowledge graph that are relevant to the context. |
| Outcome: | The proposed model outperforms a baseline language model in generating factual knowledge and generating sentences that require factual information. |
Controllable Paraphrase Generation with a Syntactic Exemplar (P19-1)
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| Challenge: | Prior work on controllable text generation assumes that the generated attribute can take on a finite set of values known a priori. |
| Approach: | They propose a task where the syntax of a generated sentence is controlled rather by a sentential exemplar. |
| Outcome: | The proposed model achieves improvements over baselines and learns to capture desirable characteristics. |
Towards Comprehensive Description Generation from Factual Attribute-value Tables (P19-1)
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| Challenge: | Existing models for comprehensive descriptions for factual attribute-value tables might suffer from missing key attributes and groundless information problems. |
| Approach: | They propose a force attention method to encourage the generator to pay more attention to uncovered attributes to avoid potential key attributes missing. |
| Outcome: | The proposed model outperforms the state-of-the-art baselines on automatic and human evaluation. |
Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation (P19-1)
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| Challenge: | Disentangling the content and style in the latent space is prevalent in text style transfer . recurrent neural networks (RNN) based encoder and decoder cannot deal with the long-term dependency . |
| Approach: | They propose a style transformer which disentangles style information in latent space . they propose encoding and decoding methods that disentangle style information . |
| Outcome: | The proposed method can achieve better style transfer and better content preservation. |
Generating Sentences from Disentangled Syntactic and Semantic Spaces (P19-1)
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| Challenge: | Variational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space. |
| Approach: | They propose to generate sentences from disentangled syntactic and semantic spaces by using the linearized tree sequence. |
| Outcome: | The proposed method achieves similar or better performance in various tasks compared with state-of-the-art models. |
Learning to Control the Fine-grained Sentiment for Story Ending Generation (P19-1)
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| Challenge: | Existing studies focus on controlling the sentiment of story endings. |
| Approach: | They propose a generic and novel framework which controls fine-grained sentiment intensity for automatic story ending generation without manually annotating sentiment labels. |
| Outcome: | The proposed framework can generate story endings which meet the given sentiment intensity better. |
Self-Attention Architectures for Answer-Agnostic Neural Question Generation (P19-1)
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| Challenge: | Neural architectures based on self-attention have attracted interest from the research community . a recent study examined the performance of Transformers on a task of Neural Question Generation . |
| Approach: | They propose to adapt Transformers to a task of Neural Question Generation without constraining the model to focus on a specific answer passage. |
| Outcome: | The proposed architectures have obtained significant improvements over the state-of-the-art in several tasks. |
Unsupervised Paraphrasing without Translation (P19-1)
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| Challenge: | Recent work on automatic paraphrasing focuses on methods leveraging machine translation as an intermediate step. |
| Approach: | They propose to learn paraphrasing models only from a monolingual corpus . they propose a residual variant of vector-quantized variational auto-encoder . |
| Outcome: | The proposed model outperforms supervised and unsupervised translation methods in paraphrase identification and training set augmentation. |
Storyboarding of Recipes: Grounded Contextual Generation (P19-1)
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| Challenge: | Using a dataset for sequential procedural (how-to) text generation from images, we show that 61% of the users found our proposed model is better than the baseline model in terms of overall recipes. |
| Approach: | They propose a dataset for sequential procedural (how-to) text generation from images in cooking domain. |
| Outcome: | The proposed model achieves a METEOR score of 0.31, an improvement of 0.6 over the baseline model. |
Negative Lexically Constrained Decoding for Paraphrase Generation (P19-1)
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| Challenge: | Paraphrase generation is a monolingual machine translation problem. |
| Approach: | They propose a neural model that first identifies words in the source sentence that should be paraphrased and then decodes them by negative lexical constraints. |
| Outcome: | The proposed model improves paraphrase generation by making necessary rewrites to an input sentence. |
Large-Scale Transfer Learning for Natural Language Generation (P19-1)
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Sergey Golovanov, Rauf Kurbanov, Sergey Nikolenko, Kyryl Truskovskyi, Alexander Tselousov, Thomas Wolf
| Challenge: | Large-scale pretrained language models define state of the art in natural language processing, achieving outstanding performance on a variety of tasks. |
| Approach: | They propose to apply large-scale pretrained language models to natural language generation tasks by comparing architectural and training schemes. |
| Outcome: | The proposed architectures perform well on open-domain dialog as a typical high entropy generation task. |
Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study (P19-1)
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| Challenge: | Sequence-to-sequence (seq2sequ) models have a weakness: they cannot always generate sentences without grammatical errors. |
| Approach: | They propose to use automatic grammatical error correction to improve seq2seq models . they conduct experiments on machine translation, formality style transfer, sentence compression and simplification . |
| Outcome: | The proposed system can improve grammaticality of generated text and improve formal style tasks. |
Improving the Robustness of Question Answering Systems to Question Paraphrasing (P19-1)
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| Challenge: | Despite advancement of question answering systems, generalizability of QA models is a topic of concern. |
| Approach: | They propose to use a neural paraphrasing model to generate multiple paraphrased questions for a given source question and a set of paraphrase suggestions to re-train the models. |
| Outcome: | The proposed approach requires no human intervention to re-train the models for improved robustness to question paraphrasing. |
RankQA: Neural Question Answering with Answer Re-Ranking (P19-1)
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| Challenge: | RankQA extends the conventional two-stage process in neural question answering . RankQ achieves state-of-the-art performance on 3 out of 4 benchmark datasets . |
| Approach: | They propose to extend the conventional two-stage process in neural QA with a third stage that performs an additional answer re-ranking. |
| Outcome: | RankQA outperforms more complex question answering systems by a significant margin on 3 out of 4 benchmark datasets. |
Latent Retrieval for Weakly Supervised Open Domain Question Answering (P19-1)
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| Challenge: | Recent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evidence candidates. |
| Approach: | They propose to jointly learn the retriever and reader from question-answer string pairs and without any IR system. |
| Outcome: | The proposed approach outperforms BM25 on open datasets with a learner and reader by 19 points in exact match. |
Multi-hop Reading Comprehension through Question Decomposition and Rescoring (P19-1)
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| Challenge: | Existing systems for multi-hop reading comprehension decompose compositional questions into simpler sub-questions . authors propose a system that learns to break compositional multi- hop questions into simple singlehop sub-question . |
| Approach: | They propose a system that decomposes a compositional question into simpler sub-questions . they propose recast subquestion generation as a span prediction problem . |
| Outcome: | The proposed system generates as effective as human-authored sub-questions using 400 examples . it also provides explainable evidence for its decision making in the form of sub-questions . |
Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge (P19-1)
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| Challenge: | Existing methods to solve Winograd Schema Challenge use only knowledge embedded in text . this limits the performance of such models on the WSC problems. |
| Approach: | They propose to augment existing language models with a commonsense knowledge hunting module and an explicit reasoning module to extract the needed knowledge from text. |
| Outcome: | The proposed system improves on the language model based methods by 5.53% and 7.7% on the dataset. |
Careful Selection of Knowledge to Solve Open Book Question Answering (P19-1)
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| Challenge: | Open book question answering requires deeper reasoning involving linguistic understanding and common knowledge. |
| Approach: | They propose a dataset that mimics open book question answering to achieve 72.0% accuracy. |
| Outcome: | The proposed dataset achieves 72.0% accuracy, an 11.6% improvement over the current state of the art. |
Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering (P19-1)
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| Challenge: | Existing approaches to detect relation detection only get high accuracy for questions whose relations have been seen in training data. |
| Approach: | They propose a method to learn representation mapping for both seen and unseen relations based on previously learned relation embedding. |
| Outcome: | The proposed method improves the performance of unseen relations while keeping the performance comparable to the state-of-the-art. |
Dynamically Fused Graph Network for Multi-hop Reasoning (P19-1)
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| Challenge: | Text-based question answering (TBQA) has been studied extensively in recent years. |
| Approach: | They propose a Dynamically Fused Graph Network to answer questions requiring multiple scattered evidence and reasoning over them. |
| Outcome: | The proposed method achieves competitive results on a public TBQA dataset and produces interpretable reasoning chains. |
NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language (P19-1)
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| Challenge: | ambiguity in natural language is difficult to interpret due to large linguistic variability. |
| Approach: | They propose to use a Prolog prover to extend neural networks with logic programming to solve multi-hop reasoning tasks over natural language. |
| Outcome: | The proposed model outperforms baseline models on two question answering tasks and is competitive on the MedHop corpus. |
Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions (P19-1)
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| Challenge: | Several deep learning models have been proposed for solving math word problems (MWPs) but their approaches to capturing features are not specifically designed for MWP. |
| Approach: | They propose to use a group attention mechanism to extract global features, quantity-related features, quantities-pair features and question-related feature in MWPs. |
| Outcome: | The proposed approach performs significantly better than previous state-of-the-art methods and boosts performance from 66.9% to 69.5% on Math23K with training-test split, from 65.8% to 66.99% on Math 23K with 5-fold cross-validation and from 69.99% to 76.1% on MAWPS. |
Synthetic QA Corpora Generation with Roundtrip Consistency (P19-1)
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| Challenge: | Existing methods for generating synthetic question answering corpora are not suitable for QA, but can be constructed from widely available natural text. |
| Approach: | They propose a method for generating synthetic question answering corpora by combining question generation and answer extraction models and filtering the results to ensure roundtrip consistency. |
| Outcome: | The proposed model achieves exact match and F1 at less than 0.1% and 0.4% from human performance on SQuAD2 and NQ. |
Are Red Roses Red? Evaluating Consistency of Question-Answering Models (P19-1)
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| Challenge: | Existing question-answering systems are limited in their ability to test reasoning and comprehension. |
| Approach: | They propose a method to automatically extract implications from QA datasets to evaluate models' consistency . they use a heuristic to generate such questions and retrain models with implication-augmented data . |
| Outcome: | The proposed method shows that generated implications are well formed and valid . retraining with implication-augmented data improves consistency on both synthetic and human-generated implications. |
MCˆ2: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension (P19-1)
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| Challenge: | Existing models combine previous questions for conversation understanding and only employ recurrent neural networks (RNN) for reasoning. |
| Approach: | They propose a multi-perspective convolutional cube model that integrates 1D and 2D convolutions with recurrent neural networks (RNN) to understand context from different perspectives. |
| Outcome: | The proposed model is based on the Conversational Question Answering (CoQA) dataset and achieves state-of-the-art results. |
Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach (P19-1)
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| Challenge: | Existing methods for reducing word omission errors in neural machine translation are prone to omit essential words on the source side. |
| Approach: | They propose a contrastive learning approach to reduce word omission errors in NMT by omitting words. |
| Outcome: | The proposed approach achieves better translation performance than baseline methods on Chinese-to-English, German-to English, and Russian-toEnglish translation tasks. |
Exploiting Sentential Context for Neural Machine Translation (P19-1)
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| Challenge: | Existing approaches to exploit sentential context for machine translation are not well studied. |
| Approach: | They propose a shallow sentential context that exploits top encoder layer, and a deep sentential one that aggregates sentential representations from all internal layers. |
| Outcome: | The proposed model outperforms the strong Transformer model on the English-German and English-French benchmarks. |
Wetin dey with these comments? Modeling Sociolinguistic Factors Affecting Code-switching Behavior in Nigerian Online Discussions (P19-1)
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| Challenge: | Multilingual individuals code switch between languages as part of a complex communication process. |
| Approach: | They propose to model the social and contextual factors eliciting code switching in a rich contextual environment by analyzing 330K articles and 389K comments labeled for code switching behavior. |
| Outcome: | The proposed model shows that topic-driven variation, tribal affiliation, emotional valence, and audience design all play complementary roles in behavior. |
Accelerating Sparse Matrix Operations in Neural Networks on Graphics Processing Units (P19-1)
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| Challenge: | Graphics Processing Units (GPUs) are used to train and evaluate neural networks efficiently. |
| Approach: | They propose two new GPU algorithms for multiplying a matrix by a few-hot vector and fused softmax and top-N selection. |
| Outcome: | The proposed algorithms achieve speedups over state-of-the-art parallel GPU baselines of up to 7x and 50x, respectively. |
An Automated Framework for Fast Cognate Detection and Bayesian Phylogenetic Inference in Computational Historical Linguistics (P19-1)
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| Challenge: | Existing methods for phylogenetic reconstruction of large datasets require time and computational power. |
| Approach: | They propose a workflow for phylogenetic reconstruction on large datasets using two methods . they use a method for fast detection of cognates and a Bayesian method for inference . their results show that the methods take less than a few minutes to process language families . |
| Outcome: | The proposed methods are fast and easy to use and close to gold standard cognate judgments and expert language family trees. |
Sentence Centrality Revisited for Unsupervised Summarization (P19-1)
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| Challenge: | Experimental results on three news summarization datasets representative of different languages and writing styles show that our approach outperforms strong baselines by a wide margin. |
| Approach: | They propose an unsupervised approach that uses a popular ranking algorithm to compute node centrality. |
| Outcome: | The proposed approach outperforms baselines on three news summarization datasets representative of different languages and writing styles. |
Discourse Representation Parsing for Sentences and Documents (P19-1)
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| Challenge: | Experimental results show that our model outperforms competitive baselines by a wide margin. |
| Approach: | They propose a neural model which parses discourse structures of arbitrary length and granularity. |
| Outcome: | The proposed model outperforms baseline models on sentence- and document-level benchmarks. |
Inducing Document Structure for Aspect-based Summarization (P19-1)
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| Challenge: | Abstractive summarization systems treat documents as unstructured and generate a single generic summary per document. |
| Approach: | They propose to incorporate document structure into automatic summarization systems . they induce latent document structure and abstractive summarizing objective . |
| Outcome: | The proposed model improves on topic-agnostic baselines and can produce abstractive and extractive aspect-based summaries. |
Incorporating Priors with Feature Attribution on Text Classification (P19-1)
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| Challenge: | Feature attribution methods are used to help users interpret complex models. |
| Approach: | They propose a feature attribution method that integrates feature attributed features into the objective function to allow machine learning practitioners to incorporate priors in model building. |
| Outcome: | The proposed method reduces undesired model biases without a tradeoff on the original task and improves classifier performance in scarce data setting. |
Matching Article Pairs with Graphical Decomposition and Convolutions (P19-1)
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| Challenge: | Existing methods for matching sentence pairs do not perform well in longer documents . Existing approaches for matching sentences do not work in longer document understanding tasks . |
| Approach: | They propose to model article pairs by comparing sentences that enclose same concept vertex . they propose to use a concept interaction graph to match articles by encoding sentences . |
| Outcome: | The proposed methods show significant improvements over existing methods . the proposed datasets consist of 30K pairs of breaking news articles . |
Hierarchical Transfer Learning for Multi-label Text Classification (P19-1)
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| Challenge: | Multi-Label Hierarchical Text Classification (MLHTC) is a task of categorizing documents into one or more topics organized in an hierarchical taxonomy. |
| Approach: | They propose a transfer learning based strategy where binary classifiers at lower levels are initialized using parameters of the parent classifier and fine-tuned on the child category classification task. |
| Outcome: | The proposed method improves on micro-F1 and macro-F1, respectively, compared to binary classifiers trained from scratch. |
Bias Analysis and Mitigation in the Evaluation of Authorship Verification (P19-1)
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| Challenge: | a paper on authorship verification shows that the underlying experiment design cannot guarantee pushing forward the state of the art. |
| Approach: | They propose a "Basic and Fairly Flawed" authorship verifier that is on a par with the best approaches submitted so far . they pinpoint sources of bias that should be eliminated and propose 'refined' authorship corpus as effective countermeasure. |
| Outcome: | The proposed approach is on par with the best approaches submitted so far . the proposed approach shows that sources of bias should be eliminated . |
Numeracy-600K: Learning Numeracy for Detecting Exaggerated Information in Market Comments (P19-1)
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| Challenge: | Numeracy is the ability to predict the magnitude of a numeral at some specific position in a text description. |
| Approach: | They propose to use a dataset to test whether neural network models can learn numeracy . numerability is the ability to predict the magnitude of a numeral at some specific position in a text description. |
| Outcome: | The proposed task can predict the magnitude of a numeral at a specific position in a text description. |
Large-Scale Multi-Label Text Classification on EU Legislation (P19-1)
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| Challenge: | Large-Scale multi-label text classification is a task of assigning to each document all the relevant labels from a large set, typically containing thousands of labels (classes). |
| Approach: | They propose to use a dataset of 57k English EU legislative documents annotated with 4.3k EUROVOC labels for LMTC, few-shot learning and contextual embeddings. |
| Outcome: | The proposed dataset is suitable for LMTC, few- and zero-shot learning and bypasses the maximum text length limit. |
Why Didn’t You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models (P19-1)
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| Challenge: | Informed prior-based methods provide better control than constraints, but constraints yield higher quality topics, but with less control. |
| Approach: | They propose to use constraints and informed prior-based methods to improve user control and topic coherence. |
| Outcome: | The proposed methods improve user control and topic coherence, while constraints yield higher quality topics, but with less control. |
Encouraging Paragraph Embeddings to Remember Sentence Identity Improves Classification (P19-1)
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| Challenge: | Existing paragraph embedding methods do not capture basic linguistic properties, but their performance is limited. |
| Approach: | They propose a paragraph embedding method that can't tell whether a sentence occurs in a given paragraph. |
| Outcome: | The proposed method outperforms reconstruction-based methods on a semi-supervised dataset and improves on benchmark datasets. |
A Multi-Task Architecture on Relevance-based Neural Query Translation (P19-1)
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| Challenge: | Existing models for cross-lingual information retrieval are not aware of the vocabulary distribution of the retrieval corpus. |
| Approach: | They propose a multi-task learning approach to train a Neural Machine Translation model with a Relevance-based Auxiliary Task (RAT) for search query translation. |
| Outcome: | The proposed model achieves 16% improvement over a strong baseline on Italian-English query-document dataset. |
Topic Modeling with Wasserstein Autoencoders (P19-1)
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| Challenge: | Existing probabilistic topic models are based on latent Dirichlet allocations and collapsed Gibbs sampling. |
| Approach: | They propose a novel topic model that enforces Dirichlet prior on latent document-topic vectors and a kernel kernel to minimize the Maximum Mean Discrepancy (MMD) They propose to measure the diversity of the produced topics and to use the widely used coherence measure NPMI to evaluate topic quality. |
| Outcome: | The proposed model performs better than existing topic models on real datasets. |
Dense Procedure Captioning in Narrated Instructional Videos (P19-1)
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| Challenge: | Existing models for video dense captioning learn video segments and generate captions without considering transcripts. |
| Approach: | They propose a model to generate procedure captions from narrated instructional videos . they extract procedures by a cross-modality module and generate captions by encoding video frames and transcripts within each extracted procedure. |
| Outcome: | The proposed model can extract procedures from narrated instructional videos and generate procedure captions by encoding video frames and transcripts. |
Latent Variable Model for Multi-modal Translation (P19-1)
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| Challenge: | Libovick and Helcl (2017) show improvements due to imposing a constraint on the KL term to promote models with non-negligible mutual information between inputs and latent variable and training on additional target-language image descriptions. |
| Approach: | They propose to model interaction between visual and textual features for multi-modal neural machine translation (MMT) using a latent variable model. |
| Outcome: | The proposed model improves over baselines including a multi-task learning approach and a conditional variational auto-encoder approach. |
Identifying Visible Actions in Lifestyle Vlogs (P19-1)
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| Challenge: | Existing methods for identifying human actions in videos are limited by the number of visual depictions in the videos. |
| Approach: | They propose a multimodal algorithm that leverages visual and linguistic clues to automatically infer which actions are visible in a video. |
| Outcome: | The proposed algorithm can identify actions visible in video while verbally describing them. |
A Corpus for Reasoning about Natural Language Grounded in Photographs (P19-1)
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| Challenge: | a dataset for visual reasoning with natural language and images is available. |
| Approach: | They propose a dataset for joint reasoning about natural language and images . they crowdsource 107,292 examples of English sentences paired with web photographs . |
| Outcome: | The proposed dataset combines 107,292 examples of English sentences with web photographs . Qualitative analysis shows the data requires compositional joint reasoning . |
Learning to Discover, Ground and Use Words with Segmental Neural Language Models (P19-1)
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| Challenge: | Existing models of word learning do not account for the long-range dependencies manifest in language and that are easily captured by recurrent neural networks. |
| Approach: | They propose a segmental neural language model that unifies word discovery, learning how words fit together to form sentences, and by conditioning the model on visual context, how words’ meanings ground in representations of nonlinguistic modalities. |
| Outcome: | The proposed model learns predictive distributions better than character LSTM models, discovers words competitively with nonparametric Bayesian word segmentation models, and improves on both. |
What Should I Ask? Using Conversationally Informative Rewards for Goal-oriented Visual Dialog. (P19-1)
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| Challenge: | a new study challenges the ability of artificial agents to engage in goal-oriented conversations . goal-orientated visual dialogue is a challenging task since it requires a strategy and contextual information to achieve a goal. |
| Approach: | They propose a goal-oriented visual dialogue system that combines reinforcement learning with regularized information gain. |
| Outcome: | The proposed system outperforms current state-of-the-art models on the GuessWhat?! dataset. |
Symbolic Inductive Bias for Visually Grounded Learning of Spoken Language (P19-1)
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| Challenge: | Existing approaches to processing spoken language are to first automatically transcribe it into text, but there is an alternative: end-to-end learning. |
| Approach: | They propose to use multitask learning to exploit existing transcribed speech within the end-to-end setting by matching spoken captions with corresponding images, speech with text, and text with images. |
| Outcome: | The proposed model improves image retrieval performance compared to training the speech/image task in isolation. |
Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog (P19-1)
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| Challenge: | Existing models for visual dialog infer the answer through multiple reasoning steps. |
| Approach: | They propose a model for visual dialog that uses multi-step reasoning to answer questions about an image. |
| Outcome: | The proposed model achieves a new state-of-the-art of 64.47% on the VisDial v1.0 dataset . |
Lattice Transformer for Speech Translation (P19-1)
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| Challenge: | Recent advances in sequence modeling have highlighted the strengths of the transformer architecture. |
| Approach: | They propose a general lattice transformer for speech translation where the input is the output of the automatic speech recognition (ASR) they propose 'controllable' lattica attention mechanism to consume latent representations. |
| Outcome: | The proposed model outperforms baseline and lattice LSTM on the Chinese-English translation task. |
Informative Image Captioning with External Sources of Information (P19-1)
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| Challenge: | Current captioning models are trained to generate captions that only contain common object names, thus falling short on an important “informativeness” dimension. |
| Approach: | They propose a mechanism for integrating image information and fine-grained labels into a caption that describes the image in a fluent and informative manner. |
| Outcome: | The proposed model integrates image information with fine-grained labels to produce fluent captions . it can control the appearance of these labels in the output, resulting in fluent and informative captions. |
CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication (P19-1)
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Jin-Hwa Kim, Nikita Kitaev, Xinlei Chen, Marcus Rohrbach, Byoung-Tak Zhang, Yuandong Tian, Dhruv Batra, Devi Parikh
| Challenge: | a goal-driven collaborative drawing task combines language, perception, and actions in a partially observable environment . et al., 1990: 138K messages exchanged between human players. |
| Approach: | They propose a goal-driven collaborative task that combines language, perception, and action . they collect a clip art dataset and use it to build an image-drawing game between two agents . |
| Outcome: | The proposed task integrates language, perception, and action in a virtual world . it is based on a dataset of 10K dialogs and 138K messages exchanged between humans . |
Bridging by Word: Image Grounded Vocabulary Construction for Visual Captioning (P19-1)
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| Challenge: | Existing research on image captioning generates frequent n-grams with irrelevant words. |
| Approach: | They propose to construct an image-grounded vocabulary incorporating visual information and relations among words into the decoding process directly. |
| Outcome: | The proposed framework is compared with state-of-the-art models on MS COCO and Flickr30k and shows that it is more efficient than existing models. |
Distilling Translations with Visual Awareness (P19-1)
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| Challenge: | Existing work on multimodal machine translation has shown that visual information is only needed in very specific cases, for example in the presence of ambiguous words where the textual context is not sufficient. |
| Approach: | They propose a translate-and-refine approach to multimodal machine translation where images are only used by a second stage decoder to generate a good first draft translation and to improve over this draft. |
| Outcome: | The proposed approach generates a good translation and improves over the draft by making better use of the target language textual context and making use of visual context. |
VIFIDEL: Evaluating the Visual Fidelity of Image Descriptions (P19-1)
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| Challenge: | Existing methods for evaluating image description generation systems are subjective and expensive to scale. |
| Approach: | They propose a new image-aware metric for evaluating image description generation systems . it estimates the faithfulness of a generated caption with respect to the content of the actual image . |
| Outcome: | The proposed metric achieves high correlation with human judgments on two well-known datasets and is competitive with metrics that depend on and rely exclusively on human references. |
Are You Looking? Grounding to Multiple Modalities in Vision-and-Language Navigation (P19-1)
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| Challenge: | Existing models that ground language into visual appearance and route structure are outperforming their visual counterparts in unseen new environments. |
| Approach: | They propose to decompose the grounding procedure into a set of expert models with access to different modalities and ensemble them at prediction time. |
| Outcome: | The proposed model outperforms models with only route structure and visual features on the benchmark Room-to-Room dataset. |
Multimodal Transformer for Unaligned Multimodal Language Sequences (P19-1)
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Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, J. Zico Kolter, Louis-Philippe Morency, Ruslan Salakhutdinov
| Challenge: | Human language is often multimodal, which comprehends a mixture of natural language, facial gestures, and acoustic behaviors. |
| Approach: | They propose a multimodal model that extends the standard Transformer network to learn representations directly from unaligned multimodal streams. |
| Outcome: | The proposed model outperforms state-of-the-art methods on aligned and non-aligned data. |
Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-ray Reports (P19-1)
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| Challenge: | Existing studies do not consider the complex structure information between and within report sections. |
| Approach: | They propose a framework which exploits the structure information between and within report sections for generating CXR imaging reports. |
| Outcome: | The proposed framework achieves state-of-the-art performance on two CXR report datasets. |
Visual Story Post-Editing (P19-1)
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| Challenge: | a dataset for human edits of machine-generated visual stories is released . it includes 14,905 human-edited versions of 2,981 machine- generated visual stories . |
| Approach: | They introduce the first dataset for human edits of machine-generated visual stories . they explore how edits may be used for the visual story post-editing task . |
| Outcome: | The proposed dataset includes 14,905 human-edited versions of 2,981 machine-generated visual stories. |
Multimodal Abstractive Summarization for How2 Videos (P19-1)
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| Challenge: | Abstractive summarization is a task of producing a shorter version of the content in the document while preserving its information. |
| Approach: | They propose a new evaluation metric that measures semantic adequacy rather than fluency of abstractive summarization tasks. |
| Outcome: | The proposed model integrates information from different sources into a coherent output. |
Learning to Relate from Captions and Bounding Boxes (P19-1)
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| Challenge: | Existing methods for classifying images without supervision are limited. |
| Approach: | They propose a top-down attention mechanism to align entities in captions to objects in the image and leverage the syntactic structure of captions for alignment. |
| Outcome: | The proposed model achieves a recall@50 of 15% and recall@100 of 25% on the relationships present in the image and predicts relations that are not present in captions. |