mtybilly/Instruct2DS huggingface dataset:
Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge
Authors: Yan Song, Xiang Ao, Fei Xia, Xiaojun Quan, Tong Zhang, Yonggang Wang Yuanhe Tian
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Chinese word segmentation (CWS) and part-of-speech (POS) tagging are important fundamental tasks for Chinese language processing, where joint learning of them is an effective one-step solution for both tasks. Previous studies for joint CWS and POS tagging mainly follow the character-based tagging paradigm with introducing contextual information such as n-gram features or sentential representations from recurrent neural models. However, for many cases, the joint tagging needs not only modeling from context features but also knowledge attached to them (e.g., syntactic relations among words); limited efforts have been made by existing research to meet such needs. In this paper, we propose a neural model named TwASP for joint CWS and POS tagging following the character-based sequence labeling paradigm, where a two-way attention mechanism is used to incorporate both context feature and their corresponding syntactic knowledge for each input character. Particularly, we use existing language processing toolkits to obtain the auto-analyzed syntactic knowledge for the context, and the proposed attention module can learn and benefit from them although their quality may not be perfect. Our experiments illustrate the effectiveness of the two-way attentions for joint CWS and POS tagging, where state-of-the-art performance is achieved on five benchmark datasets.
Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging
Authors: Nizar Habash Nasser Zalmout
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: The written forms of Semitic languages are both highly ambiguous and morphologically rich: a word can have multiple interpretations and is one of many inflected forms of the same concept or lemma. This is further exacerbated for dialectal content, which is more prone to noise and lacks a standard orthography. The morphological features can be lexicalized, like lemmas and diacritized forms, or non-lexicalized, like gender, number, and part-of-speech tags, among others. Joint modeling of the lexicalized and non-lexicalized features can identify more intricate morphological patterns, which provide better context modeling, and further disambiguate ambiguous lexical choices. However, the different modeling granularity can make joint modeling more difficult. Our approach models the different features jointly, whether lexicalized (on the character-level), or non-lexicalized (on the word-level). We use Arabic as a test case, and achieve state-of-the-art results for Modern Standard Arabic with 20% relative error reduction, and Egyptian Arabic with 11% relative error reduction.
Phonetic and Visual Priors for Decipherment of Informal Romanization
Authors: Matthew R. Gormley, Taylor Berg-Kirkpatrick Maria Ryskina
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Informal romanization is an idiosyncratic process used by humans in informal digital communication to encode non-Latin script languages into Latin character sets found on common keyboards. Character substitution choices differ between users but have been shown to be governed by the same main principles observed across a variety of languages---namely, character pairs are often associated through phonetic or visual similarity. We propose a noisy-channel WFST cascade model for deciphering the original non-Latin script from observed romanized text in an unsupervised fashion. We train our model directly on romanized data from two languages: Egyptian Arabic and Russian. We demonstrate that adding inductive bias through phonetic and visual priors on character mappings substantially improves the model`s performance on both languages, yielding results much closer to the supervised skyline. Finally, we introduce a new dataset of romanized Russian, collected from a Russian social network website and partially annotated for our experiments.
Active Learning for Coreference Resolution using Discrete Annotation
Authors: Gabriel Stanovsky, Luke Zettlemoyer Belinda Z. Li
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: We improve upon pairwise annotation for active learning in coreference resolution, by asking annotators to identify mention antecedents if a presented mention pair is deemed not coreferent. This simple modification, when combined with a novel mention clustering algorithm for selecting which examples to label, is much more efficient in terms of the performance obtained per annotation budget. In experiments with existing benchmark coreference datasets, we show that the signal from this additional question leads to significant performance gains per human-annotation hour. Future work can use our annotation protocol to effectively develop coreference models for new domains. Our code is publicly available.
Beyond Possession Existence: Duration and Co-Possession
Authors: Srikala Murugan, Eduardo Blanco Dhivya Chinnappa
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: This paper introduces two tasks: determining (a) the duration of possession relations and (b) co-possessions, i.e., whether multiple possessors possess a possessee at the same time. We present new annotations on top of corpora annotating possession existence and experimental results. Regarding possession duration, we derive the time spans we work with empirically from annotations indicating lower and upper bounds. Regarding co-possessions, we use a binary label. Cohen`s kappa coefficients indicate substantial agreement, and experimental results show that text is more useful than the image for solving these tasks.
Don`t Stop Pretraining: Adapt Language Models to Domains and Tasks
Authors: Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, Noah A. Smith Suchin Gururangan
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Language models pretrained on text from a wide variety of sources form the foundation of today`s NLP. In light of the success of these broad-coverage models, we investigate whether it is still helpful to tailor a pretrained model to the domain of a target task. We present a study across four domains (biomedical and computer science publications, news, and reviews) and eight classification tasks, showing that a second phase of pretraining in-domain (domain-adaptive pretraining) leads to performance gains, under both high- and low-resource settings. Moreover, adapting to the task`s unlabeled data (task-adaptive pretraining) improves performance even after domain-adaptive pretraining. Finally, we show that adapting to a task corpus augmented using simple data selection strategies is an effective alternative, especially when resources for domain-adaptive pretraining might be unavailable. Overall, we consistently find that multi-phase adaptive pretraining offers large gains in task performance.
Estimating Mutual Information Between Dense Word Embeddings
Authors: Aleksandar Savkov, Nils Hammerla Vitalii Zhelezniak
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Word embedding-based similarity measures are currently among the top-performing methods on unsupervised semantic textual similarity (STS) tasks. Recent work has increasingly adopted a statistical view on these embeddings, with some of the top approaches being essentially various correlations (which include the famous cosine similarity). Another excellent candidate for a similarity measure is mutual information (MI), which can capture arbitrary dependencies between the variables and has a simple and intuitive expression. Unfortunately, its use in the context of dense word embeddings has so far been avoided due to difficulties with estimating MI for continuous data. In this work we go through a vast literature on estimating MI in such cases and single out the most promising methods, yielding a simple and elegant similarity measure for word embeddings. We show that mutual information is a viable alternative to correlations, gives an excellent signal that correlates well with human judgements of similarity and rivals existing state-of-the-art unsupervised methods.
Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing
Authors: Ming-Wei Chang, Peter Shaw, Kenton Lee Alane Suhr
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: We study the task of cross-database semantic parsing (XSP), where a system that maps natural language utterances to executable SQL queries is evaluated on databases unseen during training. Recently, several datasets, including Spider, were proposed to support development of XSP systems. We propose a challenging evaluation setup for cross-database semantic parsing, focusing on variation across database schemas and in-domain language use. We re-purpose eight semantic parsing datasets that have been well-studied in the setting where in-domain training data is available, and instead use them as additional evaluation data for XSP systems instead. We build a system that performs well on Spider, and find that it struggles to generalize to our re-purposed set. Our setup uncovers several generalization challenges for cross-database semantic parsing, demonstrating the need to use and develop diverse training and evaluation datasets.
Predicting the Focus of Negation: Model and Error Analysis
Authors: Kathleen Hamilton, Alexis Palmer, Eduardo Blanco Md Mosharaf Hossain
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: The focus of a negation is the set of tokens intended to be negated, and a key component for revealing affirmative alternatives to negated utterances. In this paper, we experiment with neural networks to predict the focus of negation. Our main novelty is leveraging a scope detector to introduce the scope of negation as an additional input to the network. Experimental results show that doing so obtains the best results to date. Additionally, we perform a detailed error analysis providing insights into the main error categories, and analyze errors depending on whether the model takes into account scope and context information.
Structured Tuning for Semantic Role Labeling
Authors: Parth Anand Jawale, Martha Palmer, Vivek Srikumar Tao Li
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Recent neural network-driven semantic role labeling (SRL) systems have shown impressive improvements in F1 scores. These improvements are due to expressive input representations, which, at least at the surface, are orthogonal to knowledge-rich constrained decoding mechanisms that helped linear SRL models. Introducing the benefits of structure to inform neural models presents a methodological challenge. In this paper, we present a structured tuning framework to improve models using softened constraints only at training time. Our framework leverages the expressiveness of neural networks and provides supervision with structured loss components. We start with a strong baseline (RoBERTa) to validate the impact of our approach, and show that our framework outperforms the baseline by learning to comply with declarative constraints. Additionally, our experiments with smaller training sizes show that we can achieve consistent improvements under low-resource scenarios.
TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
Authors: Graham Neubig, Wen-tau Yih, Sebastian Riedel Pengcheng Yin
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-based natural language (NL) understanding tasks. Such models are typically trained on free-form NL text, hence may not be suitable for tasks like semantic parsing over structured data, which require reasoning over both free-form NL questions and structured tabular data (e.g., database tables). In this paper we present TaBERT, a pretrained LM that jointly learns representations for NL sentences and (semi-)structured tables. TaBERT is trained on a large corpus of 26 million tables and their English contexts. In experiments, neural semantic parsers using TaBERT as feature representation layers achieve new best results on the challenging weakly-supervised semantic parsing benchmark WikiTableQuestions, while performing competitively on the text-to-SQL dataset Spider.
Universal Decompositional Semantic Parsing
Authors: Aaron Steven White, Sheng Zhang, Benjamin Van Durme Elias Stengel-Eskin
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: We introduce a transductive model for parsing into Universal Decompositional Semantics (UDS) representations, which jointly learns to map natural language utterances into UDS graph structures and annotate the graph with decompositional semantic attribute scores. We also introduce a strong pipeline model for parsing into the UDS graph structure, and show that our transductive parser performs comparably while additionally performing attribute prediction. By analyzing the attribute prediction errors, we find the model captures natural relationships between attribute groups.
Unsupervised Cross-lingual Representation Learning at Scale
Authors: Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, Veselin Stoyanov Alexis Conneau
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6% average accuracy on XNLI, +13% average F1 score on MLQA, and +2.4% F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 15.7% in XNLI accuracy for Swahili and 11.4% for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code and models publicly available.
A Generate-and-Rank Framework with Semantic Type Regularization for Biomedical Concept Normalization
Authors: Zeyu Zhang, Steven Bethard Dongfang Xu
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Concept normalization, the task of linking textual mentions of concepts to concepts in an ontology, is challenging because ontologies are large. In most cases, annotated datasets cover only a small sample of the concepts, yet concept normalizers are expected to predict all concepts in the ontology. In this paper, we propose an architecture consisting of a candidate generator and a list-wise ranker based on BERT. The ranker considers pairings of concept mentions and candidate concepts, allowing it to make predictions for any concept, not just those seen during training. We further enhance this list-wise approach with a semantic type regularizer that allows the model to incorporate semantic type information from the ontology during training. Our proposed concept normalization framework achieves state-of-the-art performance on multiple datasets.
Hierarchical Entity Typing via Multi-level Learning to Rank
Authors: Yunmo Chen, Benjamin Van Durme Tongfei Chen
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: We propose a novel method for hierarchical entity classification that embraces ontological structure at both training and during prediction. At training, our novel multi-level learning-to-rank loss compares positive types against negative siblings according to the type tree. During prediction, we define a coarse-to-fine decoder that restricts viable candidates at each level of the ontology based on already predicted parent type(s). Our approach significantly outperform prior work on strict accuracy, demonstrating the effectiveness of our method.
Multi-Domain Named Entity Recognition with Genre-Aware and Agnostic Inference
Authors: Mayank Kulkarni, Daniel Preotiuc-Pietro Jing Wang
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Named entity recognition is a key component of many text processing pipelines and it is thus essential for this component to be robust to different types of input. However, domain transfer of NER models with data from multiple genres has not been widely studied. To this end, we conduct NER experiments in three predictive setups on data from: a) multiple domains; b) multiple domains where the genre label is unknown at inference time; c) domains not encountered in training. We introduce a new architecture tailored to this task by using shared and private domain parameters and multi-task learning. This consistently outperforms all other baseline and competitive methods on all three experimental setups, with differences ranging between +1.95 to +3.11 average F1 across multiple genres when compared to standard approaches. These results illustrate the challenges that need to be taken into account when building real-world NLP applications that are robust to various types of text and the methods that can help, at least partially, alleviate these issues.
TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories
Authors: Jun Ma, Xin Luna Dong Giannis Karamanolakis
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Extracting structured knowledge from product profiles is crucial for various applications in e-Commerce. State-of-the-art approaches for knowledge extraction were each designed for a single category of product, and thus do not apply to real-life e-Commerce scenarios, which often contain thousands of diverse categories. This paper proposes TXtract, a taxonomy-aware knowledge extraction model that applies to thousands of product categories organized in a hierarchical taxonomy. Through category conditional self-attention and multi-task learning, our approach is both scalable, as it trains a single model for thousands of categories, and effective, as it extracts category-specific attribute values. Experiments on products from a taxonomy with 4,000 categories show that TXtract outperforms state-of-the-art approaches by up to 10% in F1 and 15% in coverage across all categories.
TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition
Authors: Dong-Ho Lee, Ming Shen, Ryan Moreno, Xiao Huang, Prashant Shiralkar, Xiang Ren Bill Yuchen Lin
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Training neural models for named entity recognition (NER) in a new domain often requires additional human annotations (e.g., tens of thousands of labeled instances) that are usually expensive and time-consuming to collect. Thus, a crucial research question is how to obtain supervision in a cost-effective way. In this paper, we introduce “entity triggers,” an effective proxy of human explanations for facilitating label-efficient learning of NER models. An entity trigger is defined as a group of words in a sentence that helps to explain why humans would recognize an entity in the sentence. We crowd-sourced 14k entity triggers for two well-studied NER datasets. Our proposed model, Trigger Matching Network, jointly learns trigger representations and soft matching module with self-attention such that can generalize to unseen sentences easily for tagging. Our framework is significantly more cost-effective than the traditional neural NER frameworks. Experiments show that using only 20% of the trigger-annotated sentences results in a comparable performance as using 70% of conventional annotated sentences.
Addressing Posterior Collapse with Mutual Information for Improved Variational Neural Machine Translation
Authors: Xian Li, Jiatao Gu, Ning Dong Arya D. McCarthy
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: This paper proposes a simple and effective approach to address the problem of posterior collapse in conditional variational autoencoders (CVAEs). It thus improves performance of machine translation models that use noisy or monolingual data, as well as in conventional settings. Extending Transformer and conditional VAEs, our proposed latent variable model measurably prevents posterior collapse by (1) using a modified evidence lower bound (ELBO) objective which promotes mutual information between the latent variable and the target, and (2) guiding the latent variable with an auxiliary bag-of-words prediction task. As a result, the proposed model yields improved translation quality compared to existing variational NMT models on WMT Ro\ensuremathłeftrightarrowEn and De\ensuremathłeftrightarrowEn. With latent variables being effectively utilized, our model demonstrates improved robustness over non-latent Transformer in handling uncertainty: exploiting noisy source-side monolingual data (up to +3.2 BLEU), and training with weakly aligned web-mined parallel data (up to +4.7 BLEU).
Balancing Training for Multilingual Neural Machine Translation
Authors: Yulia Tsvetkov, Graham Neubig Xinyi Wang
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: When training multilingual machine translation (MT) models that can translate to/from multiple languages, we are faced with imbalanced training sets: some languages have much more training data than others. Standard practice is to up-sample less resourced languages to increase representation, and the degree of up-sampling has a large effect on the overall performance. In this paper, we propose a method that instead automatically learns how to weight training data through a data scorer that is optimized to maximize performance on all test languages. Experiments on two sets of languages under both one-to-many and many-to-one MT settings show our method not only consistently outperforms heuristic baselines in terms of average performance, but also offers flexible control over the performance of which languages are optimized.
Evaluating Robustness to Input Perturbations for Neural Machine Translation
Authors: Prashant Mathur, Georgiana Dinu, Yaser Al-Onaizan Xing Niu
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Neural Machine Translation (NMT) models are sensitive to small perturbations in the input. Robustness to such perturbations is typically measured using translation quality metrics such as BLEU on the noisy input. This paper proposes additional metrics which measure the relative degradation and changes in translation when small perturbations are added to the input. We focus on a class of models employing subword regularization to address robustness and perform extensive evaluations of these models using the robustness measures proposed. Results show that our proposed metrics reveal a clear trend of improved robustness to perturbations when subword regularization methods are used.
Parallel Corpus Filtering via Pre-trained Language Models
Authors: Ajay Nagesh, Kevin Knight Boliang Zhang
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Web-crawled data provides a good source of parallel corpora for training machine translation models. It is automatically obtained, but extremely noisy, and recent work shows that neural machine translation systems are more sensitive to noise than traditional statistical machine translation methods. In this paper, we propose a novel approach to filter out noisy sentence pairs from web-crawled corpora via pre-trained language models. We measure sentence parallelism by leveraging the multilingual capability of BERT and use the Generative Pre-training (GPT) language model as a domain filter to balance data domains. We evaluate the proposed method on the WMT 2018 Parallel Corpus Filtering shared task, and on our own web-crawled Japanese-Chinese parallel corpus. Our method significantly outperforms baselines and achieves a new state-of-the-art. In an unsupervised setting, our method achieves comparable performance to the top-1 supervised method. We also evaluate on a web-crawled Japanese-Chinese parallel corpus that we make publicly available.
Regularized Context Gates on Transformer for Machine Translation
Authors: Lemao Liu, Rui Wang, Guoping Huang, Max Meng Xintong Li
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Context gates are effective to control the contributions from the source and target contexts in the recurrent neural network (RNN) based neural machine translation (NMT). However, it is challenging to extend them into the advanced Transformer architecture, which is more complicated than RNN. This paper first provides a method to identify source and target contexts and then introduce a gate mechanism to control the source and target contributions in Transformer. In addition, to further reduce the bias problem in the gate mechanism, this paper proposes a regularization method to guide the learning of the gates with supervision automatically generated using pointwise mutual information. Extensive experiments on 4 translation datasets demonstrate that the proposed model obtains an averaged gain of 1.0 BLEU score over a strong Transformer baseline.
A Multi-Perspective Architecture for Semantic Code Search
Authors: Lingfei Wu, JinJun Xiong, Julia Hockenmaier Rajarshi Haldar
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: The ability to match pieces of code to their corresponding natural language descriptions and vice versa is fundamental for natural language search interfaces to software repositories. In this paper, we propose a novel multi-perspective cross-lingual neural framework for code--text matching, inspired in part by a previous model for monolingual text-to-text matching, to capture both global and local similarities. Our experiments on the CoNaLa dataset show that our proposed model yields better performance on this cross-lingual text-to-code matching task than previous approaches that map code and text to a single joint embedding space.
Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring
Authors: Diane Litman Haoran Zhang
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: While automated essay scoring (AES) can reliably grade essays at scale, automated writing evaluation (AWE) additionally provides formative feedback to guide essay revision. However, a neural AES typically does not provide useful feature representations for supporting AWE. This paper presents a method for linking AWE and neural AES, by extracting Topical Components (TCs) representing evidence from a source text using the intermediate output of attention layers. We evaluate performance using a feature-based AES requiring TCs. Results show that performance is comparable whether using automatically or manually constructed TCs for 1) representing essays as rubric-based features, 2) grading essays.
Clinical Concept Linking with Contextualized Neural Representations
Authors: Andriy Mulyar, Mark Dredze Elliot Schumacher
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: In traditional approaches to entity linking, linking decisions are based on three sources of information -- the similarity of the mention string to an entity`s name, the similarity of the context of the document to the entity, and broader information about the knowledge base (KB). In some domains, there is little contextual information present in the KB and thus we rely more heavily on mention string similarity. We consider one example of this, concept linking, which seeks to link mentions of medical concepts to a medical concept ontology. We propose an approach to concept linking that leverages recent work in contextualized neural models, such as ELMo (Peters et al. 2018), which create a token representation that integrates the surrounding context of the mention and concept name. We find a neural ranking approach paired with contextualized embeddings provides gains over a competitive baseline (Leaman et al. 2013). Additionally, we find that a pre-training step using synonyms from the ontology offers a useful initialization for the ranker.
DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking
Authors: Tuhin Chakrabarty, Tariq Alhindi, Siddharth Varia, Kriste Krstovski, Mona Diab, Smaranda Muresan Christopher Hidey
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: The increased focus on misinformation has spurred development of data and systems for detecting the veracity of a claim as well as retrieving authoritative evidence. The Fact Extraction and VERification (FEVER) dataset provides such a resource for evaluating endto- end fact-checking, requiring retrieval of evidence from Wikipedia to validate a veracity prediction. We show that current systems for FEVER are vulnerable to three categories of realistic challenges for fact-checking -- multiple propositions, temporal reasoning, and ambiguity and lexical variation -- and introduce a resource with these types of claims. Then we present a system designed to be resilient to these “attacks” using multiple pointer networks for document selection and jointly modeling a sequence of evidence sentences and veracity relation predictions. We find that in handling these attacks we obtain state-of-the-art results on FEVER, largely due to improved evidence retrieval.
Let Me Choose: From Verbal Context to Font Selection
Authors: Franck Dernoncourt, Jose Echevarria, Paul Asente, Nedim Lipka, Thamar Solorio Amirreza Shirani
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: In this paper, we aim to learn associations between visual attributes of fonts and the verbal context of the texts they are typically applied to. Compared to related work leveraging the surrounding visual context, we choose to focus only on the input text, which can enable new applications for which the text is the only visual element in the document. We introduce a new dataset, containing examples of different topics in social media posts and ads, labeled through crowd-sourcing. Due to the subjective nature of the task, multiple fonts might be perceived as acceptable for an input text, which makes this problem challenging. To this end, we investigate different end-to-end models to learn label distributions on crowd-sourced data, to capture inter-subjectivity across all annotations.
Multi-Label and Multilingual News Framing Analysis
Authors: Lei Guo, Randa Elanwar, Prakash Ishwar, Margrit Betke, Derry Tanti Wijaya Afra Feyza Akyürek
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: News framing refers to the practice in which aspects of specific issues are highlighted in the news to promote a particular interpretation. In NLP, although recent works have studied framing in English news, few have studied how the analysis can be extended to other languages and in a multi-label setting. In this work, we explore multilingual transfer learning to detect multiple frames from just the news headline in a genuinely low-resource context where there are few/no frame annotations in the target language. We propose a novel method that can leverage elementary resources consisting of a dictionary and few annotations to detect frames in the target language. Our method performs comparably or better than translating the entire target language headline to the source language for which we have annotated data. This work opens up an exciting new capability of scaling up frame analysis to many languages, even those without existing translation technologies. Lastly, we apply our method to detect frames on the issue of U.S. gun violence in multiple languages and obtain exciting insights on the relationship between different frames of the same problem across different countries with different languages.
Predicting Performance for Natural Language Processing Tasks
Authors: Antonios Anastasopoulos, Ruochen Xu, Yiming Yang, Graham Neubig Mengzhou Xia
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Given the complexity of combinations of tasks, languages, and domains in natural language processing (NLP) research, it is computationally prohibitive to exhaustively test newly proposed models on each possible experimental setting. In this work, we attempt to explore the possibility of gaining plausible judgments of how well an NLP model can perform under an experimental setting, \textitwithout actually training or testing the model. To do so, we build regression models to predict the evaluation score of an NLP experiment given the experimental settings as input. Experimenting on~9 different NLP tasks, we find that our predictors can produce meaningful predictions over unseen languages and different modeling architectures, outperforming reasonable baselines as well as human experts. %we represent experimental settings using an array of features. Going further, we outline how our predictor can be used to find a small subset of representative experiments that should be run in order to obtain plausible predictions for all other experimental settings.
ScriptWriter: Narrative-Guided Script Generation
Authors: Ruihua Song, Zhicheng Dou, Jian-Yun Nie, Jin Zhou Yutao Zhu
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: It is appealing to have a system that generates a story or scripts automatically from a storyline, even though this is still out of our reach. In dialogue systems, it would also be useful to drive dialogues by a dialogue plan. In this paper, we address a key problem involved in these applications - guiding a dialogue by a narrative. The proposed model ScriptWriter selects the best response among the candidates that fit the context as well as the given narrative. It keeps track of what in the narrative has been said and what is to be said. A narrative plays a different role than the context (i.e., previous utterances), which is generally used in current dialogue systems. Due to the unavailability of data for this new application, we construct a new large-scale data collection GraphMovie from a movie website where end- users can upload their narratives freely when watching a movie. Experimental results on the dataset show that our proposed approach based on narratives significantly outperforms the baselines that simply use the narrative as a kind of context.
Should All Cross-Lingual Embeddings Speak English?
Authors: Graham Neubig Antonios Anastasopoulos
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Most of recent work in cross-lingual word embeddings is severely Anglocentric. The vast majority of lexicon induction evaluation dictionaries are between English and another language, and the English embedding space is selected by default as the hub when learning in a multilingual setting. With this work, however, we challenge these practices. First, we show that the choice of hub language can significantly impact downstream lexicon induction zero-shot POS tagging performance. Second, we both expand a standard English-centered evaluation dictionary collection to include all language pairs using triangulation, and create new dictionaries for under-represented languages. Evaluating established methods over all these language pairs sheds light into their suitability for aligning embeddings from distant languages and presents new challenges for the field. Finally, in our analysis we identify general guidelines for strong cross-lingual embedding baselines, that extend to language pairs that do not include English.
Smart To-Do: Automatic Generation of To-Do Items from Emails
Authors: Subhabrata Mukherjee, Marcello Hasegawa, Ahmed Hassan Awadallah, Ryen White Sudipto Mukherjee
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Intelligent features in email service applications aim to increase productivity by helping people organize their folders, compose their emails and respond to pending tasks. In this work, we explore a new application, Smart-To-Do, that helps users with task management over emails. We introduce a new task and dataset for automatically generating To-Do items from emails where the sender has promised to perform an action. We design a two-stage process leveraging recent advances in neural text generation and sequence-to-sequence learning, obtaining BLEU and ROUGE scores of 0.23 and 0.63 for this task. To the best of our knowledge, this is the first work to address the problem of composing To-Do items from emails.
Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition
Authors: Alex Warstadt, Suvrat Bhooshan, Adina Williams Paloma Jeretic
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Natural language inference (NLI) is an increasingly important task for natural language understanding, which requires one to infer whether a sentence entails another. However, the ability of NLI models to make pragmatic inferences remains understudied. We create an IMPlicature and PRESupposition diagnostic dataset (IMPPRES), consisting of 32K semi-automatically generated sentence pairs illustrating well-studied pragmatic inference types. We use IMPPRES to evaluate whether BERT, InferSent, and BOW NLI models trained on MultiNLI (Williams et al., 2018) learn to make pragmatic inferences. Although MultiNLI appears to contain very few pairs illustrating these inference types, we find that BERT learns to draw pragmatic inferences. It reliably treats scalar implicatures triggered by “some” as entailments. For some presupposition triggers like “only”, BERT reliably recognizes the presupposition as an entailment, even when the trigger is embedded under an entailment canceling operator like negation. BOW and InferSent show weaker evidence of pragmatic reasoning. We conclude that NLI training encourages models to learn some, but not all, pragmatic inferences.
End-to-End Bias Mitigation by Modelling Biases in Corpora
Authors: Yonatan Belinkov, James Henderson Rabeeh Karimi Mahabadi
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Several recent studies have shown that strong natural language understanding (NLU) models are prone to relying on unwanted dataset biases without learning the underlying task, resulting in models that fail to generalize to out-of-domain datasets and are likely to perform poorly in real-world scenarios. We propose two learning strategies to train neural models, which are more robust to such biases and transfer better to out-of-domain datasets. The biases are specified in terms of one or more bias-only models, which learn to leverage the dataset biases. During training, the bias-only models' predictions are used to adjust the loss of the base model to reduce its reliance on biases by down-weighting the biased examples and focusing the training on the hard examples. We experiment on large-scale natural language inference and fact verification benchmarks, evaluating on out-of-domain datasets that are specifically designed to assess the robustness of models against known biases in the training data. Results show that our debiasing methods greatly improve robustness in all settings and better transfer to other textual entailment datasets. Our code and data are publicly available in r̆lhttps://github.com/rabeehk/robust-nli.
Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance
Authors: Nafise Sadat Moosavi, Iryna Gurevych Prasetya Ajie Utama
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Models for natural language understanding (NLU) tasks often rely on the idiosyncratic biases of the dataset, which make them brittle against test cases outside the training distribution. Recently, several proposed debiasing methods are shown to be very effective in improving out-of-distribution performance. However, their improvements come at the expense of performance drop when models are evaluated on the in-distribution data, which contain examples with higher diversity. This seemingly inevitable trade-off may not tell us much about the changes in the reasoning and understanding capabilities of the resulting models on broader types of examples beyond the small subset represented in the out-of-distribution data. In this paper, we address this trade-off by introducing a novel debiasing method, called confidence regularization, which discourage models from exploiting biases while enabling them to receive enough incentive to learn from all the training examples. We evaluate our method on three NLU tasks and show that, in contrast to its predecessors, it improves the performance on out-of-distribution datasets (e.g., 7pp gain on HANS dataset) while maintaining the original in-distribution accuracy.
NILE : Natural Language Inference with Faithful Natural Language Explanations
Authors: Partha Talukdar Sawan Kumar
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: The recent growth in the popularity and success of deep learning models on NLP classification tasks has accompanied the need for generating some form of natural language explanation of the predicted labels. Such generated natural language (NL) explanations are expected to be faithful, i.e., they should correlate well with the model`s internal decision making. In this work, we focus on the task of natural language inference (NLI) and address the following question: can we build NLI systems which produce labels with high accuracy, while also generating faithful explanations of its decisions? We propose Natural-language Inference over Label-specific Explanations (NILE), a novel NLI method which utilizes auto-generated label-specific NL explanations to produce labels along with its faithful explanation. We demonstrate NILE`s effectiveness over previously reported methods through automated and human evaluation of the produced labels and explanations. Our evaluation of NILE also supports the claim that accurate systems capable of providing testable explanations of their decisions can be designed. We discuss the faithfulness of NILE`s explanations in terms of sensitivity of the decisions to the corresponding explanations. We argue that explicit evaluation of faithfulness, in addition to label and explanation accuracy, is an important step in evaluating model`s explanations. Further, we demonstrate that task-specific probes are necessary to establish such sensitivity.
QuASE: Question-Answer Driven Sentence Encoding
Authors: Qiang Ning, Dan Roth Hangfeng He
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Question-answering (QA) data often encodes essential information in many facets. This paper studies a natural question: Can we get supervision from QA data for other tasks (typically, non-QA ones)? For example, \textitcan we use QAMR (Michael et al., 2017) to improve named entity recognition? We suggest that simply further pre-training BERT is often not the best option, and propose the \textitquestion-answer driven sentence encoding (QuASE) framework. QuASE learns representations from QA data, using BERT or other state-of-the-art contextual language models. In particular, we observe the need to distinguish between two types of sentence encodings, depending on whether the target task is a single- or multi-sentence input; in both cases, the resulting encoding is shown to be an easy-to-use plugin for many downstream tasks. This work may point out an alternative way to supervise NLP tasks.
Towards Robustifying NLI Models Against Lexical Dataset Biases
Authors: Mohit Bansal Xiang Zhou
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: While deep learning models are making fast progress on the task of Natural Language Inference, recent studies have also shown that these models achieve high accuracy by exploiting several dataset biases, and without deep understanding of the language semantics. Using contradiction-word bias and word-overlapping bias as our two bias examples, this paper explores both data-level and model-level debiasing methods to robustify models against lexical dataset biases. First, we debias the dataset through data augmentation and enhancement, but show that the model bias cannot be fully removed via this method. Next, we also compare two ways of directly debiasing the model without knowing what the dataset biases are in advance. The first approach aims to remove the label bias at the embedding level. The second approach employs a bag-of-words sub-model to capture the features that are likely to exploit the bias and prevents the original model from learning these biased features by forcing orthogonality between these two sub-models. We performed evaluations on new balanced datasets extracted from the original MNLI dataset as well as the NLI stress tests, and show that the orthogonality approach is better at debiasing the model while maintaining competitive overall accuracy.