paper_id stringlengths 17 19 | title stringlengths 23 140 | paper_url stringlengths 43 45 | authors listlengths 1 24 | abstract large_stringlengths 262 1.75k | anthology_id stringlengths 17 19 | doi stringlengths 29 31 | award stringclasses 5
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2021.naacl-main.201 | A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios | https://aclanthology.org/2021.naacl-main.201/ | [
"Michael A. Hedderich",
"Lukas Lange",
"Heike Adel",
"Jannik Strötgen",
"Dietrich Klakow"
] | Deep neural networks and huge language models are becoming omnipresent in natural language applications. As they are known for requiring large amounts of training data, there is a growing body of work to improve the performance in low-resource settings. Motivated by the recent fundamental changes towards neural models ... | 2021.naacl-main.201 | 10.18653/v1/2021.naacl-main.201 | null | 2010.12309 | title_snapshot |
2021.naacl-main.202 | Temporal Knowledge Graph Completion using a Linear Temporal Regularizer and Multivector Embeddings | https://aclanthology.org/2021.naacl-main.202/ | [
"Chengjin Xu",
"Yung-Yu Chen",
"Mojtaba Nayyeri",
"Jens Lehmann"
] | Representation learning approaches for knowledge graphs have been mostly designed for static data. However, many knowledge graphs involve evolving data, e.g., the fact (The President of the United States is Barack Obama) is valid only from 2009 to 2017. This introduces important challenges for knowledge representation ... | 2021.naacl-main.202 | 10.18653/v1/2021.naacl-main.202 | null | null | null |
2021.naacl-main.203 | UDALM: Unsupervised Domain Adaptation through Language Modeling | https://aclanthology.org/2021.naacl-main.203/ | [
"Constantinos Karouzos",
"Georgios Paraskevopoulos",
"Alexandros Potamianos"
] | In this work we explore Unsupervised Domain Adaptation (UDA) of pretrained language models for downstream tasks. We introduce UDALM, a fine-tuning procedure, using a mixed classification and Masked Language Model loss, that can adapt to the target domain distribution in a robust and sample efficient manner. Our experim... | 2021.naacl-main.203 | 10.18653/v1/2021.naacl-main.203 | null | 2104.07078 | title_snapshot |
2021.naacl-main.204 | Beyond Black & White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning | https://aclanthology.org/2021.naacl-main.204/ | [
"Tommaso Fornaciari",
"Alexandra Uma",
"Silviu Paun",
"Barbara Plank",
"Dirk Hovy",
"Massimo Poesio"
] | Supervised learning assumes that a ground truth label exists. However, the reliability of this ground truth depends on human annotators, who often disagree. Prior work has shown that this disagreement can be helpful in training models. We propose a novel method to incorporate this disagreement as information: in additi... | 2021.naacl-main.204 | 10.18653/v1/2021.naacl-main.204 | null | null | null |
2021.naacl-main.205 | Clustering-based Inference for Biomedical Entity Linking | https://aclanthology.org/2021.naacl-main.205/ | [
"Rico Angell",
"Nicholas Monath",
"Sunil Mohan",
"Nishant Yadav",
"Andrew McCallum"
] | Due to large number of entities in biomedical knowledge bases, only a small fraction of entities have corresponding labelled training data. This necessitates entity linking models which are able to link mentions of unseen entities using learned representations of entities. Previous approaches link each mention independ... | 2021.naacl-main.205 | 10.18653/v1/2021.naacl-main.205 | null | 2010.11253 | title_snapshot |
2021.naacl-main.206 | Variance-reduced First-order Meta-learning for Natural Language Processing Tasks | https://aclanthology.org/2021.naacl-main.206/ | [
"Lingxiao Wang",
"Kevin Huang",
"Tengyu Ma",
"Quanquan Gu",
"Jing Huang"
] | First-order meta-learning algorithms have been widely used in practice to learn initial model parameters that can be quickly adapted to new tasks due to their efficiency and effectiveness. However, existing studies find that meta-learner can overfit to some specific adaptation when we have heterogeneous tasks, leading ... | 2021.naacl-main.206 | 10.18653/v1/2021.naacl-main.206 | null | null | null |
2021.naacl-main.207 | Diversity-Aware Batch Active Learning for Dependency Parsing | https://aclanthology.org/2021.naacl-main.207/ | [
"Tianze Shi",
"Adrian Benton",
"Igor Malioutov",
"Ozan İrsoy"
] | While the predictive performance of modern statistical dependency parsers relies heavily on the availability of expensive expert-annotated treebank data, not all annotations contribute equally to the training of the parsers. In this paper, we attempt to reduce the number of labeled examples needed to train a strong dep... | 2021.naacl-main.207 | 10.18653/v1/2021.naacl-main.207 | null | 2104.13936 | title_snapshot |
2021.naacl-main.208 | How many data points is a prompt worth? | https://aclanthology.org/2021.naacl-main.208/ | [
"Teven Le Scao",
"Alexander Rush"
] | When fine-tuning pretrained models for classification, researchers either use a generic model head or a task-specific prompt for prediction. Proponents of prompting have argued that prompts provide a method for injecting task-specific guidance, which is beneficial in low-data regimes. We aim to quantify this benefit th... | 2021.naacl-main.208 | 10.18653/v1/2021.naacl-main.208 | Outstanding Short Paper | 2103.08493 | title_snapshot |
2021.naacl-main.209 | Can Latent Alignments Improve Autoregressive Machine Translation? | https://aclanthology.org/2021.naacl-main.209/ | [
"Adi Haviv",
"Lior Vassertail",
"Omer Levy"
] | Latent alignment objectives such as CTC and AXE significantly improve non-autoregressive machine translation models. Can they improve autoregressive models as well? We explore the possibility of training autoregressive machine translation models with latent alignment objectives, and observe that, in practice, this appr... | 2021.naacl-main.209 | 10.18653/v1/2021.naacl-main.209 | null | 2104.09554 | title_snapshot |
2021.naacl-main.210 | Smoothing and Shrinking the Sparse Seq2Seq Search Space | https://aclanthology.org/2021.naacl-main.210/ | [
"Ben Peters",
"André F. T. Martins"
] | Current sequence-to-sequence models are trained to minimize cross-entropy and use softmax to compute the locally normalized probabilities over target sequences. While this setup has led to strong results in a variety of tasks, one unsatisfying aspect is its length bias: models give high scores to short, inadequate hypo... | 2021.naacl-main.210 | 10.18653/v1/2021.naacl-main.210 | null | 2103.10291 | title_snapshot |
2021.naacl-main.211 | Unified Pre-training for Program Understanding and Generation | https://aclanthology.org/2021.naacl-main.211/ | [
"Wasi Ahmad",
"Saikat Chakraborty",
"Baishakhi Ray",
"Kai-Wei Chang"
] | Code summarization and generation empower conversion between programming language (PL) and natural language (NL), while code translation avails the migration of legacy code from one PL to another. This paper introduces PLBART, a sequence-to-sequence model capable of performing a broad spectrum of program and language u... | 2021.naacl-main.211 | 10.18653/v1/2021.naacl-main.211 | null | 2103.06333 | title_snapshot |
2021.naacl-main.212 | Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding | https://aclanthology.org/2021.naacl-main.212/ | [
"Ting Hua",
"Yilin Shen",
"Changsheng Zhao",
"Yen-Chang Hsu",
"Hongxia Jin"
] | Domain classification is the fundamental task in natural language understanding (NLU), which often requires fast accommodation to new emerging domains. This constraint makes it impossible to retrain all previous domains, even if they are accessible to the new model. Most existing continual learning approaches suffer fr... | 2021.naacl-main.212 | 10.18653/v1/2021.naacl-main.212 | null | 2201.01420 | title_snapshot |
2021.naacl-main.213 | On the Embeddings of Variables in Recurrent Neural Networks for Source Code | https://aclanthology.org/2021.naacl-main.213/ | [
"Nadezhda Chirkova"
] | Source code processing heavily relies on the methods widely used in natural language processing (NLP), but involves specifics that need to be taken into account to achieve higher quality. An example of this specificity is that the semantics of a variable is defined not only by its name but also by the contexts in which... | 2021.naacl-main.213 | 10.18653/v1/2021.naacl-main.213 | null | 2010.12693 | title_snapshot |
2021.naacl-main.214 | Cross-Lingual Word Embedding Refinement by \ell_{1} Norm Optimisation | https://aclanthology.org/2021.naacl-main.214/ | [
"Xutan Peng",
"Chenghua Lin",
"Mark Stevenson"
] | Cross-Lingual Word Embeddings (CLWEs) encode words from two or more languages in a shared high-dimensional space in which vectors representing words with similar meaning (regardless of language) are closely located. Existing methods for building high-quality CLWEs learn mappings that minimise the ℓ2 norm loss function.... | 2021.naacl-main.214 | 10.18653/v1/2021.naacl-main.214 | null | 2104.04916 | title_snapshot |
2021.naacl-main.215 | Semantic Frame Forecast | https://aclanthology.org/2021.naacl-main.215/ | [
"Chieh-Yang Huang",
"Ting-Hao Huang"
] | This paper introduces Semantic Frame Forecast, a task that predicts the semantic frames that will occur in the next 10, 100, or even 1,000 sentences in a running story. Prior work focused on predicting the immediate future of a story, such as one to a few sentences ahead. However, when novelists write long stories, gen... | 2021.naacl-main.215 | 10.18653/v1/2021.naacl-main.215 | null | 2104.05604 | title_snapshot |
2021.naacl-main.216 | MUSER: MUltimodal Stress detection using Emotion Recognition as an Auxiliary Task | https://aclanthology.org/2021.naacl-main.216/ | [
"Yiqun Yao",
"Michalis Papakostas",
"Mihai Burzo",
"Mohamed Abouelenien",
"Rada Mihalcea"
] | The capability to automatically detect human stress can benefit artificial intelligent agents involved in affective computing and human-computer interaction. Stress and emotion are both human affective states, and stress has proven to have important implications on the regulation and expression of emotion. Although a s... | 2021.naacl-main.216 | 10.18653/v1/2021.naacl-main.216 | null | 2105.08146 | title_snapshot |
2021.naacl-main.217 | Learning to Decompose and Organize Complex Tasks | https://aclanthology.org/2021.naacl-main.217/ | [
"Yi Zhang",
"Sujay Kumar Jauhar",
"Julia Kiseleva",
"Ryen White",
"Dan Roth"
] | People rely on digital task management tools, such as email or to-do apps, to manage their tasks. Some of these tasks are large and complex, leading to action paralysis and feelings of being overwhelmed on the part of the user. The micro-productivity literature has shown that such tasks could benefit from being decompo... | 2021.naacl-main.217 | 10.18653/v1/2021.naacl-main.217 | null | null | null |
2021.naacl-main.218 | Continual Learning for Text Classification with Information Disentanglement Based Regularization | https://aclanthology.org/2021.naacl-main.218/ | [
"Yufan Huang",
"Yanzhe Zhang",
"Jiaao Chen",
"Xuezhi Wang",
"Diyi Yang"
] | Continual learning has become increasingly important as it enables NLP models to constantly learn and gain knowledge over time. Previous continual learning methods are mainly designed to preserve knowledge from previous tasks, without much emphasis on how to well generalize models to new tasks. In this work, we propose... | 2021.naacl-main.218 | 10.18653/v1/2021.naacl-main.218 | null | 2104.05489 | title_snapshot |
2021.naacl-main.219 | Learning from Executions for Semantic Parsing | https://aclanthology.org/2021.naacl-main.219/ | [
"Bailin Wang",
"Mirella Lapata",
"Ivan Titov"
] | Semantic parsing aims at translating natural language (NL) utterances onto machine-interpretable programs, which can be executed against a real-world environment. The expensive annotation of utterance-program pairs has long been acknowledged as a major bottleneck for the deployment of contemporary neural models to real... | 2021.naacl-main.219 | 10.18653/v1/2021.naacl-main.219 | null | 2104.05819 | title_snapshot |
2021.naacl-main.220 | Learning to Synthesize Data for Semantic Parsing | https://aclanthology.org/2021.naacl-main.220/ | [
"Bailin Wang",
"Wenpeng Yin",
"Xi Victoria Lin",
"Caiming Xiong"
] | Synthesizing data for semantic parsing has gained increasing attention recently. However, most methods require handcrafted (high-precision) rules in their generative process, hindering the exploration of diverse unseen data. In this work, we propose a generative model which features a (non-neural) PCFG that models the ... | 2021.naacl-main.220 | 10.18653/v1/2021.naacl-main.220 | null | 2104.05827 | title_snapshot |
2021.naacl-main.221 | Edge: Enriching Knowledge Graph Embeddings with External Text | https://aclanthology.org/2021.naacl-main.221/ | [
"Saed Rezayi",
"Handong Zhao",
"Sungchul Kim",
"Ryan Rossi",
"Nedim Lipka",
"Sheng Li"
] | Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods. While there is an abundance of textual information throughout the web and many existing knowledge bases, aligning information across these diverse data sources remains a challenge in the literature. Previou... | 2021.naacl-main.221 | 10.18653/v1/2021.naacl-main.221 | null | 2104.04909 | title_snapshot |
2021.naacl-main.222 | FLIN: A Flexible Natural Language Interface for Web Navigation | https://aclanthology.org/2021.naacl-main.222/ | [
"Sahisnu Mazumder",
"Oriana Riva"
] | AI assistants can now carry out tasks for users by directly interacting with website UIs. Current semantic parsing and slot-filling techniques cannot flexibly adapt to many different websites without being constantly re-trained. We propose FLIN, a natural language interface for web navigation that maps user commands to... | 2021.naacl-main.222 | 10.18653/v1/2021.naacl-main.222 | null | 2010.12844 | title_snapshot |
2021.naacl-main.223 | Game-theoretic Vocabulary Selection via the Shapley Value and Banzhaf Index | https://aclanthology.org/2021.naacl-main.223/ | [
"Roma Patel",
"Marta Garnelo",
"Ian Gemp",
"Chris Dyer",
"Yoram Bachrach"
] | The input vocabulary and the representations learned are crucial to the performance of neural NLP models. Using the full vocabulary results in less explainable and more memory intensive models, with the embedding layer often constituting the majority of model parameters. It is thus common to use a smaller vocabulary to... | 2021.naacl-main.223 | 10.18653/v1/2021.naacl-main.223 | null | null | null |
2021.naacl-main.224 | Incorporating External Knowledge to Enhance Tabular Reasoning | https://aclanthology.org/2021.naacl-main.224/ | [
"J. Neeraja",
"Vivek Gupta",
"Vivek Srikumar"
] | Reasoning about tabular information presents unique challenges to modern NLP approaches which largely rely on pre-trained contextualized embeddings of text. In this paper, we study these challenges through the problem of tabular natural language inference. We propose easy and effective modifications to how information ... | 2021.naacl-main.224 | 10.18653/v1/2021.naacl-main.224 | null | 2104.04243 | title_snapshot |
2021.naacl-main.225 | Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention | https://aclanthology.org/2021.naacl-main.225/ | [
"Pengcheng Yin",
"Hao Fang",
"Graham Neubig",
"Adam Pauls",
"Emmanouil Antonios Platanios",
"Yu Su",
"Sam Thomson",
"Jacob Andreas"
] | We describe a span-level supervised attention loss that improves compositional generalization in semantic parsers. Our approach builds on existing losses that encourage attention maps in neural sequence-to-sequence models to imitate the output of classical word alignment algorithms. Where past work has used word-level ... | 2021.naacl-main.225 | 10.18653/v1/2021.naacl-main.225 | null | null | null |
2021.naacl-main.226 | Domain Adaptation for Arabic Cross-Domain and Cross-Dialect Sentiment Analysis from Contextualized Word Embedding | https://aclanthology.org/2021.naacl-main.226/ | [
"Abdellah El Mekki",
"Abdelkader El Mahdaouy",
"Ismail Berrada",
"Ahmed Khoumsi"
] | Finetuning deep pre-trained language models has shown state-of-the-art performances on a wide range of Natural Language Processing (NLP) applications. Nevertheless, their generalization performance drops under domain shift. In the case of Arabic language, diglossia makes building and annotating corpora for each dialect... | 2021.naacl-main.226 | 10.18653/v1/2021.naacl-main.226 | null | null | null |
2021.naacl-main.227 | Multi-task Learning of Negation and Speculation for Targeted Sentiment Classification | https://aclanthology.org/2021.naacl-main.227/ | [
"Andrew Moore",
"Jeremy Barnes"
] | The majority of work in targeted sentiment analysis has concentrated on finding better methods to improve the overall results. Within this paper we show that these models are not robust to linguistic phenomena, specifically negation and speculation. In this paper, we propose a multi-task learning method to incorporate ... | 2021.naacl-main.227 | 10.18653/v1/2021.naacl-main.227 | null | 2010.08318 | title_snapshot |
2021.naacl-main.228 | A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User Reviews | https://aclanthology.org/2021.naacl-main.228/ | [
"Gabriele Pergola",
"Lin Gui",
"Yulan He"
] | The flexibility of the inference process in Variational Autoencoders (VAEs) has recently led to revising traditional probabilistic topic models giving rise to Neural Topic Models (NTM). Although these approaches have achieved significant results, surprisingly very little work has been done on how to disentangle the lat... | 2021.naacl-main.228 | 10.18653/v1/2021.naacl-main.228 | null | 2010.11384 | title_snapshot |
2021.naacl-main.229 | Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification | https://aclanthology.org/2021.naacl-main.229/ | [
"Xiaochen Hou",
"Peng Qi",
"Guangtao Wang",
"Rex Ying",
"Jing Huang",
"Xiaodong He",
"Bowen Zhou"
] | Recent work on aspect-level sentiment classification has demonstrated the efficacy of incorporating syntactic structures such as dependency trees with graph neural networks (GNN), but these approaches are usually vulnerable to parsing errors. To better leverage syntactic information in the face of unavoidable errors, w... | 2021.naacl-main.229 | 10.18653/v1/2021.naacl-main.229 | null | 2103.11794 | title_snapshot |
2021.naacl-main.230 | Emotion-Infused Models for Explainable Psychological Stress Detection | https://aclanthology.org/2021.naacl-main.230/ | [
"Elsbeth Turcan",
"Smaranda Muresan",
"Kathleen McKeown"
] | The problem of detecting psychological stress in online posts, and more broadly, of detecting people in distress or in need of help, is a sensitive application for which the ability to interpret models is vital. Here, we present work exploring the use of a semantically related task, emotion detection, for equally compe... | 2021.naacl-main.230 | 10.18653/v1/2021.naacl-main.230 | null | null | null |
2021.naacl-main.231 | Aspect-based Sentiment Analysis with Type-aware Graph Convolutional Networks and Layer Ensemble | https://aclanthology.org/2021.naacl-main.231/ | [
"Yuanhe Tian",
"Guimin Chen",
"Yan Song"
] | It is popular that neural graph-based models are applied in existing aspect-based sentiment analysis (ABSA) studies for utilizing word relations through dependency parses to facilitate the task with better semantic guidance for analyzing context and aspect words. However, most of these studies only leverage dependency ... | 2021.naacl-main.231 | 10.18653/v1/2021.naacl-main.231 | null | null | null |
2021.naacl-main.232 | Supertagging-based Parsing with Linear Context-free Rewriting Systems | https://aclanthology.org/2021.naacl-main.232/ | [
"Thomas Ruprecht",
"Richard Mörbitz"
] | We present the first supertagging-based parser for linear context-free rewriting systems (LCFRS). It utilizes neural classifiers and outperforms previous LCFRS-based parsers in both accuracy and parsing speed by a wide margin. Our results keep up with the best (general) discontinuous parsers, particularly the scores fo... | 2021.naacl-main.232 | 10.18653/v1/2021.naacl-main.232 | null | 2010.10238 | title_snapshot |
2021.naacl-main.233 | Outside Computation with Superior Functions | https://aclanthology.org/2021.naacl-main.233/ | [
"Parker Riley",
"Daniel Gildea"
] | We show that a general algorithm for efficient computation of outside values under the minimum of superior functions framework proposed by Knuth (1977) would yield a sub-exponential time algorithm for SAT, violating the Strong Exponential Time Hypothesis (SETH). | 2021.naacl-main.233 | 10.18653/v1/2021.naacl-main.233 | null | null | null |
2021.naacl-main.234 | Learning Syntax from Naturally-Occurring Bracketings | https://aclanthology.org/2021.naacl-main.234/ | [
"Tianze Shi",
"Ozan İrsoy",
"Igor Malioutov",
"Lillian Lee"
] | Naturally-occurring bracketings, such as answer fragments to natural language questions and hyperlinks on webpages, can reflect human syntactic intuition regarding phrasal boundaries. Their availability and approximate correspondence to syntax make them appealing as distant information sources to incorporate into unsup... | 2021.naacl-main.234 | 10.18653/v1/2021.naacl-main.234 | null | 2104.13933 | title_snapshot |
2021.naacl-main.235 | Bot-Adversarial Dialogue for Safe Conversational Agents | https://aclanthology.org/2021.naacl-main.235/ | [
"Jing Xu",
"Da Ju",
"Margaret Li",
"Y-Lan Boureau",
"Jason Weston",
"Emily Dinan"
] | Conversational agents trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior. We introduce a new human-and-model-in-the-loop framework for evaluating the toxicity of such models, and compare a variety of existing meth... | 2021.naacl-main.235 | 10.18653/v1/2021.naacl-main.235 | null | null | null |
2021.naacl-main.236 | Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog | https://aclanthology.org/2021.naacl-main.236/ | [
"Arun Babu",
"Akshat Shrivastava",
"Armen Aghajanyan",
"Ahmed Aly",
"Angela Fan",
"Marjan Ghazvininejad"
] | Semantic parsing using sequence-to-sequence models allows parsing of deeper representations compared to traditional word tagging based models. In spite of these advantages, widespread adoption of these models for real-time conversational use cases has been stymied by higher compute requirements and thus higher latency.... | 2021.naacl-main.236 | 10.18653/v1/2021.naacl-main.236 | null | 2104.04923 | title_snapshot |
2021.naacl-main.237 | Example-Driven Intent Prediction with Observers | https://aclanthology.org/2021.naacl-main.237/ | [
"Shikib Mehri",
"Mihail Eric"
] | A key challenge of dialog systems research is to effectively and efficiently adapt to new domains. A scalable paradigm for adaptation necessitates the development of generalizable models that perform well in few-shot settings. In this paper, we focus on the intent classification problem which aims to identify user inte... | 2021.naacl-main.237 | 10.18653/v1/2021.naacl-main.237 | null | 2010.08684 | title_snapshot |
2021.naacl-main.238 | Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog Management | https://aclanthology.org/2021.naacl-main.238/ | [
"Zhengxu Hou",
"Bang Liu",
"Ruihui Zhao",
"Zijing Ou",
"Yafei Liu",
"Xi Chen",
"Yefeng Zheng"
] | For task-oriented dialog systems, training a Reinforcement Learning (RL) based Dialog Management module suffers from low sample efficiency and slow convergence speed due to the sparse rewards in RL. To solve this problem, many strategies have been proposed to give proper rewards when training RL, but their rewards lack... | 2021.naacl-main.238 | 10.18653/v1/2021.naacl-main.238 | null | 2104.04748 | title_snapshot |
2021.naacl-main.239 | Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems | https://aclanthology.org/2021.naacl-main.239/ | [
"Derek Chen",
"Howard Chen",
"Yi Yang",
"Alexander Lin",
"Zhou Yu"
] | Existing goal-oriented dialogue datasets focus mainly on identifying slots and values. However, customer support interactions in reality often involve agents following multi-step procedures derived from explicitly-defined company policies as well. To study customer service dialogue systems in more realistic settings, w... | 2021.naacl-main.239 | 10.18653/v1/2021.naacl-main.239 | null | 2104.00783 | title_snapshot |
2021.naacl-main.240 | Controlling Dialogue Generation with Semantic Exemplars | https://aclanthology.org/2021.naacl-main.240/ | [
"Prakhar Gupta",
"Jeffrey Bigham",
"Yulia Tsvetkov",
"Amy Pavel"
] | Dialogue systems pretrained with large language models generate locally coherent responses, but lack fine-grained control over responses necessary to achieve specific goals. A promising method to control response generation is exemplar-based generation, in which models edit exemplar responses that are retrieved from tr... | 2021.naacl-main.240 | 10.18653/v1/2021.naacl-main.240 | null | 2008.09075 | title_snapshot |
2021.naacl-main.241 | COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List | https://aclanthology.org/2021.naacl-main.241/ | [
"Luyu Gao",
"Zhuyun Dai",
"Jamie Callan"
] | Classical information retrieval systems such as BM25 rely on exact lexical match and can carry out search efficiently with inverted list index. Recent neural IR models shifts towards soft matching all query document terms, but they lose the computation efficiency of exact match systems. This paper presents COIL, a cont... | 2021.naacl-main.241 | 10.18653/v1/2021.naacl-main.241 | null | 2104.07186 | title_snapshot |
2021.naacl-main.242 | X-Class: Text Classification with Extremely Weak Supervision | https://aclanthology.org/2021.naacl-main.242/ | [
"Zihan Wang",
"Dheeraj Mekala",
"Jingbo Shang"
] | In this paper, we explore text classification with extremely weak supervision, i.e., only relying on the surface text of class names. This is a more challenging setting than the seed-driven weak supervision, which allows a few seed words per class. We opt to attack this problem from a representation learning perspectiv... | 2021.naacl-main.242 | 10.18653/v1/2021.naacl-main.242 | null | 2010.12794 | title_snapshot |
2021.naacl-main.243 | Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modeling | https://aclanthology.org/2021.naacl-main.243/ | [
"Aaron Mueller",
"Mark Dredze"
] | Neural topic models can augment or replace bag-of-words inputs with the learned representations of deep pre-trained transformer-based word prediction models. One added benefit when using representations from multilingual models is that they facilitate zero-shot polylingual topic modeling. However, while it has been wid... | 2021.naacl-main.243 | 10.18653/v1/2021.naacl-main.243 | null | 2104.05064 | title_snapshot |
2021.naacl-main.244 | Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification | https://aclanthology.org/2021.naacl-main.244/ | [
"Wilson Fearn",
"Orion Weller",
"Kevin Seppi"
] | Text classification is a significant branch of natural language processing, and has many applications including document classification and sentiment analysis. Unsurprisingly, those who do text classification are concerned with the run-time of their algorithms, many of which depend on the size of the corpus’ vocabulary... | 2021.naacl-main.244 | 10.18653/v1/2021.naacl-main.244 | null | 2104.03848 | title_snapshot |
2021.naacl-main.245 | Faithfully Explainable Recommendation via Neural Logic Reasoning | https://aclanthology.org/2021.naacl-main.245/ | [
"Yaxin Zhu",
"Yikun Xian",
"Zuohui Fu",
"Gerard de Melo",
"Yongfeng Zhang"
] | Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation process. However, prior research rarely considers the faithfulness of the derived explanations to justify the decision-making process. To the ... | 2021.naacl-main.245 | 10.18653/v1/2021.naacl-main.245 | null | 2104.07869 | title_snapshot |
2021.naacl-main.246 | You Sound Like Someone Who Watches Drama Movies: Towards Predicting Movie Preferences from Conversational Interactions | https://aclanthology.org/2021.naacl-main.246/ | [
"Sergey Volokhin",
"Joyce Ho",
"Oleg Rokhlenko",
"Eugene Agichtein"
] | The increasing popularity of voice-based personal assistants provides new opportunities for conversational recommendation. One particularly interesting area is movie recommendation, which can benefit from an open-ended interaction with the user, through a natural conversation. We explore one promising direction for con... | 2021.naacl-main.246 | 10.18653/v1/2021.naacl-main.246 | null | null | null |
2021.naacl-main.247 | Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents | https://aclanthology.org/2021.naacl-main.247/ | [
"Shunyu Yao",
"Karthik Narasimhan",
"Matthew Hausknecht"
] | Text-based games simulate worlds and interact with players using natural language. Recent work has used them as a testbed for autonomous language-understanding agents, with the motivation being that understanding the meanings of words or semantics is a key component of how humans understand, reason, and act in these wo... | 2021.naacl-main.247 | 10.18653/v1/2021.naacl-main.247 | null | 2103.13552 | title_snapshot |
2021.naacl-main.248 | SOrT-ing VQA Models : Contrastive Gradient Learning for Improved Consistency | https://aclanthology.org/2021.naacl-main.248/ | [
"Sameer Dharur",
"Purva Tendulkar",
"Dhruv Batra",
"Devi Parikh",
"Ramprasaath R. Selvaraju"
] | Recent research in Visual Question Answering (VQA) has revealed state-of-the-art models to be inconsistent in their understanding of the world - they answer seemingly difficult questions requiring reasoning correctly but get simpler associated sub-questions wrong. These sub-questions pertain to lower level visual conce... | 2021.naacl-main.248 | 10.18653/v1/2021.naacl-main.248 | null | 2010.10038 | title_snapshot |
2021.naacl-main.249 | Semi-Supervised Policy Initialization for Playing Games with Language Hints | https://aclanthology.org/2021.naacl-main.249/ | [
"Tsu-Jui Fu",
"William Yang Wang"
] | Using natural language as a hint can supply an additional reward for playing sparse-reward games. Achieving a goal should involve several different hints, while the given hints are usually incomplete. Those unmentioned latent hints still rely on the sparse reward signal, and make the learning process difficult. In this... | 2021.naacl-main.249 | 10.18653/v1/2021.naacl-main.249 | null | null | null |
2021.naacl-main.250 | Revisiting Document Representations for Large-Scale Zero-Shot Learning | https://aclanthology.org/2021.naacl-main.250/ | [
"Jihyung Kil",
"Wei-Lun Chao"
] | Zero-shot learning aims to recognize unseen objects using their semantic representations. Most existing works use visual attributes labeled by humans, not suitable for large-scale applications. In this paper, we revisit the use of documents as semantic representations. We argue that documents like Wikipedia pages conta... | 2021.naacl-main.250 | 10.18653/v1/2021.naacl-main.250 | null | 2104.10355 | title_snapshot |
2021.naacl-main.251 | Negative language transfer in learner English: A new dataset | https://aclanthology.org/2021.naacl-main.251/ | [
"Leticia Farias Wanderley",
"Nicole Zhao",
"Carrie Demmans Epp"
] | Automatic personalized corrective feedback can help language learners from different backgrounds better acquire a new language. This paper introduces a learner English dataset in which learner errors are accompanied by information about possible error sources. This dataset contains manually annotated error causes for l... | 2021.naacl-main.251 | 10.18653/v1/2021.naacl-main.251 | null | null | null |
2021.naacl-main.252 | SentSim: Crosslingual Semantic Evaluation of Machine Translation | https://aclanthology.org/2021.naacl-main.252/ | [
"Yurun Song",
"Junchen Zhao",
"Lucia Specia"
] | Machine translation (MT) is currently evaluated in one of two ways: in a monolingual fashion, by comparison with the system output to one or more human reference translations, or in a trained crosslingual fashion, by building a supervised model to predict quality scores from human-labeled data. In this paper, we propos... | 2021.naacl-main.252 | 10.18653/v1/2021.naacl-main.252 | null | null | null |
2021.naacl-main.253 | Quality Estimation for Image Captions Based on Large-scale Human Evaluations | https://aclanthology.org/2021.naacl-main.253/ | [
"Tomer Levinboim",
"Ashish V. Thapliyal",
"Piyush Sharma",
"Radu Soricut"
] | Automatic image captioning has improved significantly over the last few years, but the problem is far from being solved, with state of the art models still often producing low quality captions when used in the wild. In this paper, we focus on the task of Quality Estimation (QE) for image captions, which attempts to mod... | 2021.naacl-main.253 | 10.18653/v1/2021.naacl-main.253 | null | 1909.03396 | title_snapshot |
2021.naacl-main.254 | CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems | https://aclanthology.org/2021.naacl-main.254/ | [
"Kushal Chawla",
"Jaysa Ramirez",
"Rene Clever",
"Gale Lucas",
"Jonathan May",
"Jonathan Gratch"
] | Automated systems that negotiate with humans have broad applications in pedagogy and conversational AI. To advance the development of practical negotiation systems, we present CaSiNo: a novel corpus of over a thousand negotiation dialogues in English. Participants take the role of campsite neighbors and negotiate for f... | 2021.naacl-main.254 | 10.18653/v1/2021.naacl-main.254 | null | 2103.15721 | title_snapshot |
2021.naacl-main.255 | News Headline Grouping as a Challenging NLU Task | https://aclanthology.org/2021.naacl-main.255/ | [
"Philippe Laban",
"Lucas Bandarkar",
"Marti A. Hearst"
] | Recent progress in Natural Language Understanding (NLU) has seen the latest models outperform human performance on many standard tasks. These impressive results have led the community to introspect on dataset limitations, and iterate on more nuanced challenges. In this paper, we introduce the task of HeadLine Grouping ... | 2021.naacl-main.255 | 10.18653/v1/2021.naacl-main.255 | null | 2105.05391 | title_snapshot |
2021.naacl-main.256 | Olá, Bonjour, Salve! XFORMAL: A Benchmark for Multilingual Formality Style Transfer | https://aclanthology.org/2021.naacl-main.256/ | [
"Eleftheria Briakou",
"Di Lu",
"Ke Zhang",
"Joel Tetreault"
] | We take the first step towards multilingual style transfer by creating and releasing XFORMAL, a benchmark of multiple formal reformulations of informal text in Brazilian Portuguese, French, and Italian. Results on XFORMAL suggest that state-of-the-art style transfer approaches perform close to simple baselines, indicat... | 2021.naacl-main.256 | 10.18653/v1/2021.naacl-main.256 | null | 2104.04108 | title_judge |
2021.naacl-main.257 | Grouping Words with Semantic Diversity | https://aclanthology.org/2021.naacl-main.257/ | [
"Karine Chubarian",
"Abdul Rafae Khan",
"Anastasios Sidiropoulos",
"Jia Xu"
] | Deep Learning-based NLP systems can be sensitive to unseen tokens and hard to learn with high-dimensional inputs, which critically hinder learning generalization. We introduce an approach by grouping input words based on their semantic diversity to simplify input language representation with low ambiguity. Since the se... | 2021.naacl-main.257 | 10.18653/v1/2021.naacl-main.257 | null | null | null |
2021.naacl-main.258 | Noise Stability Regularization for Improving BERT Fine-tuning | https://aclanthology.org/2021.naacl-main.258/ | [
"Hang Hua",
"Xingjian Li",
"Dejing Dou",
"Chengzhong Xu",
"Jiebo Luo"
] | Fine-tuning pre-trained language models suchas BERT has become a common practice dom-inating leaderboards across various NLP tasks. Despite its recent success and wide adoption,this process is unstable when there are onlya small number of training samples available. The brittleness of this process is often reflectedby ... | 2021.naacl-main.258 | 10.18653/v1/2021.naacl-main.258 | null | 2107.04835 | title_snapshot |
2021.naacl-main.259 | FlowPrior: Learning Expressive Priors for Latent Variable Sentence Models | https://aclanthology.org/2021.naacl-main.259/ | [
"Xiaoan Ding",
"Kevin Gimpel"
] | Variational autoencoders (VAEs) are widely used for latent variable modeling of text. We focus on variations that learn expressive prior distributions over the latent variable. We find that existing training strategies are not effective for learning rich priors, so we propose adding the importance-sampled log marginal ... | 2021.naacl-main.259 | 10.18653/v1/2021.naacl-main.259 | null | null | null |
2021.naacl-main.260 | HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization | https://aclanthology.org/2021.naacl-main.260/ | [
"Zhongfen Deng",
"Hao Peng",
"Dongxiao He",
"Jianxin Li",
"Philip Yu"
] | The current state-of-the-art model HiAGM for hierarchical text classification has two limitations. First, it correlates each text sample with all labels in the dataset which contains irrelevant information. Second, it does not consider any statistical constraint on the label representations learned by the structure enc... | 2021.naacl-main.260 | 10.18653/v1/2021.naacl-main.260 | null | 2104.05220 | title_snapshot |
2021.naacl-main.261 | Knowledge Guided Metric Learning for Few-Shot Text Classification | https://aclanthology.org/2021.naacl-main.261/ | [
"Dianbo Sui",
"Yubo Chen",
"Binjie Mao",
"Delai Qiu",
"Kang Liu",
"Jun Zhao"
] | Humans can distinguish new categories very efficiently with few examples, largely due to the fact that human beings can leverage knowledge obtained from relevant tasks. However, deep learning based text classification model tends to struggle to achieve satisfactory performance when labeled data are scarce. Inspired by ... | 2021.naacl-main.261 | 10.18653/v1/2021.naacl-main.261 | null | 2004.01907 | title_snapshot |
2021.naacl-main.262 | Ensemble of MRR and NDCG models for Visual Dialog | https://aclanthology.org/2021.naacl-main.262/ | [
"Idan Schwartz"
] | Assessing an AI agent that can converse in human language and understand visual content is challenging. Generation metrics, such as BLEU scores favor correct syntax over semantics. Hence a discriminative approach is often used, where an agent ranks a set of candidate options. The mean reciprocal rank (MRR) metric evalu... | 2021.naacl-main.262 | 10.18653/v1/2021.naacl-main.262 | null | 2104.07511 | title_snapshot |
2021.naacl-main.263 | Supervised Neural Clustering via Latent Structured Output Learning: Application to Question Intents | https://aclanthology.org/2021.naacl-main.263/ | [
"Iryna Haponchyk",
"Alessandro Moschitti"
] | Previous pre-neural work on structured prediction has produced very effective supervised clustering algorithms using linear classifiers, e.g., structured SVM or perceptron. However, these cannot exploit the representation learning ability of neural networks, which would make supervised clustering even more powerful, i.... | 2021.naacl-main.263 | 10.18653/v1/2021.naacl-main.263 | null | null | null |
2021.naacl-main.264 | ConVEx: Data-Efficient and Few-Shot Slot Labeling | https://aclanthology.org/2021.naacl-main.264/ | [
"Matthew Henderson",
"Ivan Vulić"
] | We propose ConVEx (Conversational Value Extractor), an efficient pretraining and fine-tuning neural approach for slot-labeling dialog tasks. Instead of relying on more general pretraining objectives from prior work (e.g., language modeling, response selection), ConVEx’s pretraining objective, a novel pairwise cloze tas... | 2021.naacl-main.264 | 10.18653/v1/2021.naacl-main.264 | null | 2010.11791 | title_snapshot |
2021.naacl-main.265 | CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues | https://aclanthology.org/2021.naacl-main.265/ | [
"Bo-Hsiang Tseng",
"Shruti Bhargava",
"Jiarui Lu",
"Joel Ruben Antony Moniz",
"Dhivya Piraviperumal",
"Lin Li",
"Hong Yu"
] | Anaphora and ellipses are two common phenomena in dialogues. Without resolving referring expressions and information omission, dialogue systems may fail to generate consistent and coherent responses. Traditionally, anaphora is resolved by coreference resolution and ellipses by query rewrite. In this work, we propose a ... | 2021.naacl-main.265 | 10.18653/v1/2021.naacl-main.265 | null | 2105.09914 | title_snapshot |
2021.naacl-main.266 | Knowledge-Driven Slot Constraints for Goal-Oriented Dialogue Systems | https://aclanthology.org/2021.naacl-main.266/ | [
"Piyawat Lertvittayakumjorn",
"Daniele Bonadiman",
"Saab Mansour"
] | In goal-oriented dialogue systems, users provide information through slot values to achieve specific goals. Practically, some combinations of slot values can be invalid according to external knowledge. For example, a combination of “cheese pizza” (a menu item) and “oreo cookies” (a topping) from an input utterance “Can... | 2021.naacl-main.266 | 10.18653/v1/2021.naacl-main.266 | null | null | null |
2021.naacl-main.267 | Clipping Loops for Sample-Efficient Dialogue Policy Optimisation | https://aclanthology.org/2021.naacl-main.267/ | [
"Yen-Chen Wu",
"Carl Edward Rasmussen"
] | Training dialogue agents requires a large number of interactions with users: agents have no idea about which responses are bad among a lengthy dialogue. In this paper, we propose loop-clipping policy optimisation (LCPO) to eliminate useless responses. LCPO consists of two stages: loop clipping and advantage clipping. I... | 2021.naacl-main.267 | 10.18653/v1/2021.naacl-main.267 | null | null | null |
2021.naacl-main.268 | Integrating Lexical Information into Entity Neighbourhood Representations for Relation Prediction | https://aclanthology.org/2021.naacl-main.268/ | [
"Ian Wood",
"Mark Johnson",
"Stephen Wan"
] | Relation prediction informed from a combination of text corpora and curated knowledge bases, combining knowledge graph completion with relation extraction, is a relatively little studied task. A system that can perform this task has the ability to extend an arbitrary set of relational database tables with information e... | 2021.naacl-main.268 | 10.18653/v1/2021.naacl-main.268 | null | null | null |
2021.naacl-main.269 | Noisy-Labeled NER with Confidence Estimation | https://aclanthology.org/2021.naacl-main.269/ | [
"Kun Liu",
"Yao Fu",
"Chuanqi Tan",
"Mosha Chen",
"Ningyu Zhang",
"Songfang Huang",
"Sheng Gao"
] | Recent studies in deep learning have shown significant progress in named entity recognition (NER). However, most existing works assume clean data annotation, while real-world scenarios typically involve a large amount of noises from a variety of sources (e.g., pseudo, weak, or distant annotations). This work studies NE... | 2021.naacl-main.269 | 10.18653/v1/2021.naacl-main.269 | null | 2104.04318 | title_snapshot |
2021.naacl-main.270 | TABBIE: Pretrained Representations of Tabular Data | https://aclanthology.org/2021.naacl-main.270/ | [
"Hiroshi Iida",
"Dung Thai",
"Varun Manjunatha",
"Mohit Iyyer"
] | Existing work on tabular representation-learning jointly models tables and associated text using self-supervised objective functions derived from pretrained language models such as BERT. While this joint pretraining improves tasks involving paired tables and text (e.g., answering questions about tables), we show that i... | 2021.naacl-main.270 | 10.18653/v1/2021.naacl-main.270 | null | 2105.02584 | title_snapshot |
2021.naacl-main.271 | Better Feature Integration for Named Entity Recognition | https://aclanthology.org/2021.naacl-main.271/ | [
"Lu Xu",
"Zhanming Jie",
"Wei Lu",
"Lidong Bing"
] | It has been shown that named entity recognition (NER) could benefit from incorporating the long-distance structured information captured by dependency trees. We believe this is because both types of features - the contextual information captured by the linear sequences and the structured information captured by the dep... | 2021.naacl-main.271 | 10.18653/v1/2021.naacl-main.271 | null | 2104.05316 | title_snapshot |
2021.naacl-main.272 | ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning | https://aclanthology.org/2021.naacl-main.272/ | [
"Chih-Yao Chen",
"Cheng-Te Li"
] | While relation extraction is an essential task in knowledge acquisition and representation, and new-generated relations are common in the real world, less effort is made to predict unseen relations that cannot be observed at the training stage. In this paper, we formulate the zero-shot relation extraction problem by in... | 2021.naacl-main.272 | 10.18653/v1/2021.naacl-main.272 | null | 2104.04697 | title_snapshot |
2021.naacl-main.273 | Graph Convolutional Networks for Event Causality Identification with Rich Document-level Structures | https://aclanthology.org/2021.naacl-main.273/ | [
"Minh Tran Phu",
"Thien Huu Nguyen"
] | We study the problem of Event Causality Identification (ECI) to detect causal relation between event mention pairs in text. Although deep learning models have recently shown state-of-the-art performance for ECI, they are limited to the intra-sentence setting where event mention pairs are presented in the same sentences... | 2021.naacl-main.273 | 10.18653/v1/2021.naacl-main.273 | null | null | null |
2021.naacl-main.274 | A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution | https://aclanthology.org/2021.naacl-main.274/ | [
"Tuan Lai",
"Heng Ji",
"Trung Bui",
"Quan Hung Tran",
"Franck Dernoncourt",
"Walter Chang"
] | Event coreference resolution is an important research problem with many applications. Despite the recent remarkable success of pre-trained language models, we argue that it is still highly beneficial to utilize symbolic features for the task. However, as the input for coreference resolution typically comes from upstrea... | 2021.naacl-main.274 | 10.18653/v1/2021.naacl-main.274 | null | 2104.01697 | title_snapshot |
2021.naacl-main.275 | Multi-Style Transfer with Discriminative Feedback on Disjoint Corpus | https://aclanthology.org/2021.naacl-main.275/ | [
"Navita Goyal",
"Balaji Vasan Srinivasan",
"Anandhavelu N",
"Abhilasha Sancheti"
] | Style transfer has been widely explored in natural language generation with non-parallel corpus by directly or indirectly extracting a notion of style from source and target domain corpus. A common shortcoming of existing approaches is the prerequisite of joint annotations across all the stylistic dimensions under cons... | 2021.naacl-main.275 | 10.18653/v1/2021.naacl-main.275 | null | 2010.11578 | title_snapshot |
2021.naacl-main.276 | FUDGE: Controlled Text Generation With Future Discriminators | https://aclanthology.org/2021.naacl-main.276/ | [
"Kevin Yang",
"Dan Klein"
] | We propose Future Discriminators for Generation (FUDGE), a flexible and modular method for controlled text generation. Given a pre-existing model G for generating text from a distribution of interest, FUDGE enables conditioning on a desired attribute a (for example, formality) while requiring access only to G’s output ... | 2021.naacl-main.276 | 10.18653/v1/2021.naacl-main.276 | null | 2104.05218 | title_snapshot |
2021.naacl-main.277 | Controllable Text Simplification with Explicit Paraphrasing | https://aclanthology.org/2021.naacl-main.277/ | [
"Mounica Maddela",
"Fernando Alva-Manchego",
"Wei Xu"
] | Text Simplification improves the readability of sentences through several rewriting transformations, such as lexical paraphrasing, deletion, and splitting. Current simplification systems are predominantly sequence-to-sequence models that are trained end-to-end to perform all these operations simultaneously. However, su... | 2021.naacl-main.277 | 10.18653/v1/2021.naacl-main.277 | null | 2010.11004 | title_snapshot |
2021.naacl-main.278 | Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training | https://aclanthology.org/2021.naacl-main.278/ | [
"Oshin Agarwal",
"Heming Ge",
"Siamak Shakeri",
"Rami Al-Rfou"
] | Prior work on Data-To-Text Generation, the task of converting knowledge graph (KG) triples into natural text, focused on domain-specific benchmark datasets. In this paper, however, we verbalize the entire English Wikidata KG, and discuss the unique challenges associated with a broad, open-domain, large-scale verbalizat... | 2021.naacl-main.278 | 10.18653/v1/2021.naacl-main.278 | null | 2010.12688 | title_snapshot |
2021.naacl-main.279 | Choose Your Own Adventure: Paired Suggestions in Collaborative Writing for Evaluating Story Generation Models | https://aclanthology.org/2021.naacl-main.279/ | [
"Elizabeth Clark",
"Noah A. Smith"
] | Story generation is an open-ended and subjective task, which poses a challenge for evaluating story generation models. We present Choose Your Own Adventure, a collaborative writing setup for pairwise model evaluation. Two models generate suggestions to people as they write a short story; we ask writers to choose one of... | 2021.naacl-main.279 | 10.18653/v1/2021.naacl-main.279 | null | null | null |
2021.naacl-main.280 | InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training | https://aclanthology.org/2021.naacl-main.280/ | [
"Zewen Chi",
"Li Dong",
"Furu Wei",
"Nan Yang",
"Saksham Singhal",
"Wenhui Wang",
"Xia Song",
"Xian-Ling Mao",
"Heyan Huang",
"Ming Zhou"
] | In this work, we present an information-theoretic framework that formulates cross-lingual language model pre-training as maximizing mutual information between multilingual-multi-granularity texts. The unified view helps us to better understand the existing methods for learning cross-lingual representations. More import... | 2021.naacl-main.280 | 10.18653/v1/2021.naacl-main.280 | null | 2007.07834 | title_snapshot |
2021.naacl-main.281 | Context-Interactive Pre-Training for Document Machine Translation | https://aclanthology.org/2021.naacl-main.281/ | [
"Pengcheng Yang",
"Pei Zhang",
"Boxing Chen",
"Jun Xie",
"Weihua Luo"
] | Document machine translation aims to translate the source sentence into the target language in the presence of additional contextual information. However, it typically suffers from a lack of doc-level bilingual data. To remedy this, here we propose a simple yet effective context-interactive pre-training approach, which... | 2021.naacl-main.281 | 10.18653/v1/2021.naacl-main.281 | null | null | null |
2021.naacl-main.282 | Code-Mixing on Sesame Street: Dawn of the Adversarial Polyglots | https://aclanthology.org/2021.naacl-main.282/ | [
"Samson Tan",
"Shafiq Joty"
] | Multilingual models have demonstrated impressive cross-lingual transfer performance. However, test sets like XNLI are monolingual at the example level. In multilingual communities, it is common for polyglots to code-mix when conversing with each other. Inspired by this phenomenon, we present two strong black-box advers... | 2021.naacl-main.282 | 10.18653/v1/2021.naacl-main.282 | null | 2103.09593 | title_snapshot |
2021.naacl-main.283 | X-METRA-ADA: Cross-lingual Meta-Transfer learning Adaptation to Natural Language Understanding and Question Answering | https://aclanthology.org/2021.naacl-main.283/ | [
"Meryem M’hamdi",
"Doo Soon Kim",
"Franck Dernoncourt",
"Trung Bui",
"Xiang Ren",
"Jonathan May"
] | Multilingual models, such as M-BERT and XLM-R, have gained increasing popularity, due to their zero-shot cross-lingual transfer learning capabilities. However, their generalization ability is still inconsistent for typologically diverse languages and across different benchmarks. Recently, meta-learning has garnered att... | 2021.naacl-main.283 | 10.18653/v1/2021.naacl-main.283 | null | 2104.09696 | title_snapshot |
2021.naacl-main.284 | Explicit Alignment Objectives for Multilingual Bidirectional Encoders | https://aclanthology.org/2021.naacl-main.284/ | [
"Junjie Hu",
"Melvin Johnson",
"Orhan Firat",
"Aditya Siddhant",
"Graham Neubig"
] | Pre-trained cross-lingual encoders such as mBERT (Devlin et al., 2019) and XLM-R (Conneau et al., 2020) have proven impressively effective at enabling transfer-learning of NLP systems from high-resource languages to low-resource languages. This success comes despite the fact that there is no explicit objective to align... | 2021.naacl-main.284 | 10.18653/v1/2021.naacl-main.284 | null | 2010.07972 | title_snapshot |
2021.naacl-main.285 | Cross-lingual Cross-modal Pretraining for Multimodal Retrieval | https://aclanthology.org/2021.naacl-main.285/ | [
"Hongliang Fei",
"Tan Yu",
"Ping Li"
] | Recent pretrained vision-language models have achieved impressive performance on cross-modal retrieval tasks in English. Their success, however, heavily depends on the availability of many annotated image-caption datasets for pretraining, where the texts are not necessarily in English. Although we can utilize machine t... | 2021.naacl-main.285 | 10.18653/v1/2021.naacl-main.285 | null | null | null |
2021.naacl-main.286 | Wikipedia Entities as Rendezvous across Languages: Grounding Multilingual Language Models by Predicting Wikipedia Hyperlinks | https://aclanthology.org/2021.naacl-main.286/ | [
"Iacer Calixto",
"Alessandro Raganato",
"Tommaso Pasini"
] | Masked language models have quickly become the de facto standard when processing text. Recently, several approaches have been proposed to further enrich word representations with external knowledge sources such as knowledge graphs. However, these models are devised and evaluated in a monolingual setting only. In this w... | 2021.naacl-main.286 | 10.18653/v1/2021.naacl-main.286 | null | null | null |
2021.naacl-main.287 | multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning | https://aclanthology.org/2021.naacl-main.287/ | [
"Swarnadeep Saha",
"Prateek Yadav",
"Mohit Bansal"
] | We focus on a type of linguistic formal reasoning where the goal is to reason over explicit knowledge in the form of natural language facts and rules (Clark et al., 2020). A recent work, named PRover (Saha et al., 2020), performs such reasoning by answering a question and also generating a proof graph that explains the... | 2021.naacl-main.287 | 10.18653/v1/2021.naacl-main.287 | null | 2106.01354 | title_snapshot |
2021.naacl-main.288 | Adaptable and Interpretable Neural MemoryOver Symbolic Knowledge | https://aclanthology.org/2021.naacl-main.288/ | [
"Pat Verga",
"Haitian Sun",
"Livio Baldini Soares",
"William Cohen"
] | Past research has demonstrated that large neural language models (LMs) encode surprising amounts of factual information: however, augmenting or modifying this information requires modifying a corpus and retraining, which is computationally expensive. To address this problem, we develop a neural LM that includes an inte... | 2021.naacl-main.288 | 10.18653/v1/2021.naacl-main.288 | null | 2007.00849 | title_judge |
2021.naacl-main.289 | CLEVR_HYP: A Challenge Dataset and Baselines for Visual Question Answering with Hypothetical Actions over Images | https://aclanthology.org/2021.naacl-main.289/ | [
"Shailaja Keyur Sampat",
"Akshay Kumar",
"Yezhou Yang",
"Chitta Baral"
] | Most existing research on visual question answering (VQA) is limited to information explicitly present in an image or a video. In this paper, we take visual understanding to a higher level where systems are challenged to answer questions that involve mentally simulating the hypothetical consequences of performing speci... | 2021.naacl-main.289 | 10.18653/v1/2021.naacl-main.289 | null | 2104.05981 | title_snapshot |
2021.naacl-main.290 | Refining Targeted Syntactic Evaluation of Language Models | https://aclanthology.org/2021.naacl-main.290/ | [
"Benjamin Newman",
"Kai-Siang Ang",
"Julia Gong",
"John Hewitt"
] | Targeted syntactic evaluation of subject-verb number agreement in English (TSE) evaluates language models’ syntactic knowledge using hand-crafted minimal pairs of sentences that differ only in the main verb’s conjugation. The method evaluates whether language models rate each grammatical sentence as more likely than it... | 2021.naacl-main.290 | 10.18653/v1/2021.naacl-main.290 | null | 2104.09635 | title_snapshot |
2021.naacl-main.291 | Universal Adversarial Attacks with Natural Triggers for Text Classification | https://aclanthology.org/2021.naacl-main.291/ | [
"Liwei Song",
"Xinwei Yu",
"Hsuan-Tung Peng",
"Karthik Narasimhan"
] | Recent work has demonstrated the vulnerability of modern text classifiers to universal adversarial attacks, which are input-agnostic sequences of words added to text processed by classifiers. Despite being successful, the word sequences produced in such attacks are often ungrammatical and can be easily distinguished fr... | 2021.naacl-main.291 | 10.18653/v1/2021.naacl-main.291 | null | 2005.00174 | title_snapshot |
2021.naacl-main.292 | QuadrupletBERT: An Efficient Model For Embedding-Based Large-Scale Retrieval | https://aclanthology.org/2021.naacl-main.292/ | [
"Peiyang Liu",
"Sen Wang",
"Xi Wang",
"Wei Ye",
"Shikun Zhang"
] | The embedding-based large-scale query-document retrieval problem is a hot topic in the information retrieval (IR) field. Considering that pre-trained language models like BERT have achieved great success in a wide variety of NLP tasks, we present a QuadrupletBERT model for effective and efficient retrieval in this pape... | 2021.naacl-main.292 | 10.18653/v1/2021.naacl-main.292 | null | null | null |
2021.naacl-main.293 | Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial Attack | https://aclanthology.org/2021.naacl-main.293/ | [
"Liwen Wang",
"Yuanmeng Yan",
"Keqing He",
"Yanan Wu",
"Weiran Xu"
] | Representation learning is widely used in NLP for a vast range of tasks. However, representations derived from text corpora often reflect social biases. This phenomenon is pervasive and consistent across different neural models, causing serious concern. Previous methods mostly rely on a pre-specified, user-provided dir... | 2021.naacl-main.293 | 10.18653/v1/2021.naacl-main.293 | null | null | null |
2021.naacl-main.294 | An Empirical Investigation of Bias in the Multimodal Analysis of Financial Earnings Calls | https://aclanthology.org/2021.naacl-main.294/ | [
"Ramit Sawhney",
"Arshiya Aggarwal",
"Rajiv Ratn Shah"
] | Volatility prediction is complex due to the stock market’s stochastic nature. Existing research focuses on the textual elements of financial disclosures like earnings calls transcripts to forecast stock volatility and risk, but ignores the rich acoustic features in the company executives’ speech. Recently, new multimod... | 2021.naacl-main.294 | 10.18653/v1/2021.naacl-main.294 | null | null | null |
2021.naacl-main.295 | Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing | https://aclanthology.org/2021.naacl-main.295/ | [
"Boaz Shmueli",
"Jan Fell",
"Soumya Ray",
"Lun-Wei Ku"
] | The use of crowdworkers in NLP research is growing rapidly, in tandem with the exponential increase in research production in machine learning and AI. Ethical discussion regarding the use of crowdworkers within the NLP research community is typically confined in scope to issues related to labor conditions such as fair ... | 2021.naacl-main.295 | 10.18653/v1/2021.naacl-main.295 | null | 2104.10097 | title_snapshot |
2021.naacl-main.296 | On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning | https://aclanthology.org/2021.naacl-main.296/ | [
"Xisen Jin",
"Francesco Barbieri",
"Brendan Kennedy",
"Aida Mostafazadeh Davani",
"Leonardo Neves",
"Xiang Ren"
] | Fine-tuned language models have been shown to exhibit biases against protected groups in a host of modeling tasks such as text classification and coreference resolution. Previous works focus on detecting these biases, reducing bias in data representations, and using auxiliary training objectives to mitigate bias during... | 2021.naacl-main.296 | 10.18653/v1/2021.naacl-main.296 | null | 2010.12864 | title_snapshot |
2021.naacl-main.297 | Case Study: Deontological Ethics in NLP | https://aclanthology.org/2021.naacl-main.297/ | [
"Shrimai Prabhumoye",
"Brendon Boldt",
"Ruslan Salakhutdinov",
"Alan W Black"
] | Recent work in natural language processing (NLP) has focused on ethical challenges such as understanding and mitigating bias in data and algorithms; identifying objectionable content like hate speech, stereotypes and offensive language; and building frameworks for better system design and data handling practices. Howev... | 2021.naacl-main.297 | 10.18653/v1/2021.naacl-main.297 | null | 2010.04658 | title_snapshot |
2021.naacl-main.298 | Privacy Regularization: Joint Privacy-Utility Optimization in LanguageModels | https://aclanthology.org/2021.naacl-main.298/ | [
"Fatemehsadat Mireshghallah",
"Huseyin Inan",
"Marcello Hasegawa",
"Victor Rühle",
"Taylor Berg-Kirkpatrick",
"Robert Sim"
] | Neural language models are known to have a high capacity for memorization of training samples. This may have serious privacy im- plications when training models on user content such as email correspondence. Differential privacy (DP), a popular choice to train models with privacy guarantees, comes with significant costs... | 2021.naacl-main.298 | 10.18653/v1/2021.naacl-main.298 | null | 2103.07567 | title_judge |
2021.naacl-main.299 | On the Impact of Random Seeds on the Fairness of Clinical Classifiers | https://aclanthology.org/2021.naacl-main.299/ | [
"Silvio Amir",
"Jan-Willem van de Meent",
"Byron Wallace"
] | Recent work has shown that fine-tuning large networks is surprisingly sensitive to changes in random seed(s). We explore the implications of this phenomenon for model fairness across demographic groups in clinical prediction tasks over electronic health records (EHR) in MIMIC-III —— the standard dataset in clinical NLP... | 2021.naacl-main.299 | 10.18653/v1/2021.naacl-main.299 | null | 2104.06338 | title_snapshot |
2021.naacl-main.300 | Topic Model or Topic Twaddle? Re-evaluating Semantic Interpretability Measures | https://aclanthology.org/2021.naacl-main.300/ | [
"Caitlin Doogan",
"Wray Buntine"
] | When developing topic models, a critical question that should be asked is: How well will this model work in an applied setting? Because standard performance evaluation of topic interpretability uses automated measures modeled on human evaluation tests that are dissimilar to applied usage, these models’ generalizability... | 2021.naacl-main.300 | 10.18653/v1/2021.naacl-main.300 | null | null | null |
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