NAACL
Collection
Accepted papers for NAACL (Annual Conference of the North American Chapter of the Association for Computational Linguistics), one dataset per year. • 9 items • Updated
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
values | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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2021.naacl-main.1 | Knowledge Router: Learning Disentangled Representations for Knowledge Graphs | https://aclanthology.org/2021.naacl-main.1/ | [
"Shuai Zhang",
"Xi Rao",
"Yi Tay",
"Ce Zhang"
] | The design of expressive representations of entities and relations in a knowledge graph is an important endeavor. While many of the existing approaches have primarily focused on learning from relational patterns and structural information, the intrinsic complexity of KG entities has been more or less overlooked. More c... | 2021.naacl-main.1 | 10.18653/v1/2021.naacl-main.1 | null | null | null |
2021.naacl-main.2 | Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors | https://aclanthology.org/2021.naacl-main.2/ | [
"Fenia Christopoulou",
"Makoto Miwa",
"Sophia Ananiadou"
] | We propose a multi-task, probabilistic approach to facilitate distantly supervised relation extraction by bringing closer the representations of sentences that contain the same Knowledge Base pairs. To achieve this, we bias the latent space of sentences via a Variational Autoencoder (VAE) that is trained jointly with a... | 2021.naacl-main.2 | 10.18653/v1/2021.naacl-main.2 | null | 2104.08225 | title_snapshot |
2021.naacl-main.3 | Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks | https://aclanthology.org/2021.naacl-main.3/ | [
"Minh Van Nguyen",
"Viet Dac Lai",
"Thien Huu Nguyen"
] | Existing works on information extraction (IE) have mainly solved the four main tasks separately (entity mention recognition, relation extraction, event trigger detection, and argument extraction), thus failing to benefit from inter-dependencies between tasks. This paper presents a novel deep learning model to simultane... | 2021.naacl-main.3 | 10.18653/v1/2021.naacl-main.3 | null | 2103.09330 | title_snapshot |
2021.naacl-main.4 | Abstract Meaning Representation Guided Graph Encoding and Decoding for Joint Information Extraction | https://aclanthology.org/2021.naacl-main.4/ | [
"Zixuan Zhang",
"Heng Ji"
] | The tasks of Rich Semantic Parsing, such as Abstract Meaning Representation (AMR), share similar goals with Information Extraction (IE) to convert natural language texts into structured semantic representations. To take advantage of such similarity, we propose a novel AMR-guided framework for joint information extracti... | 2021.naacl-main.4 | 10.18653/v1/2021.naacl-main.4 | null | null | null |
2021.naacl-main.5 | A Frustratingly Easy Approach for Entity and Relation Extraction | https://aclanthology.org/2021.naacl-main.5/ | [
"Zexuan Zhong",
"Danqi Chen"
] | End-to-end relation extraction aims to identify named entities and extract relations between them. Most recent work models these two subtasks jointly, either by casting them in one structured prediction framework, or performing multi-task learning through shared representations. In this work, we present a simple pipeli... | 2021.naacl-main.5 | 10.18653/v1/2021.naacl-main.5 | null | 2010.12812 | title_snapshot |
2021.naacl-main.6 | Event Time Extraction and Propagation via Graph Attention Networks | https://aclanthology.org/2021.naacl-main.6/ | [
"Haoyang Wen",
"Yanru Qu",
"Heng Ji",
"Qiang Ning",
"Jiawei Han",
"Avi Sil",
"Hanghang Tong",
"Dan Roth"
] | Grounding events into a precise timeline is important for natural language understanding but has received limited attention in recent work. This problem is challenging due to the inherent ambiguity of language and the requirement for information propagation over inter-related events. This paper first formulates this pr... | 2021.naacl-main.6 | 10.18653/v1/2021.naacl-main.6 | null | null | null |
2021.naacl-main.7 | Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers | https://aclanthology.org/2021.naacl-main.7/ | [
"Hongfei Xu",
"Josef van Genabith",
"Qiuhui Liu",
"Deyi Xiong"
] | Due to its effectiveness and performance, the Transformer translation model has attracted wide attention, most recently in terms of probing-based approaches. Previous work focuses on using or probing source linguistic features in the encoder. To date, the way word translation evolves in Transformer layers has not yet b... | 2021.naacl-main.7 | 10.18653/v1/2021.naacl-main.7 | null | 2003.09586 | title_snapshot |
2021.naacl-main.8 | Mediators in Determining what Processing BERT Performs First | https://aclanthology.org/2021.naacl-main.8/ | [
"Aviv Slobodkin",
"Leshem Choshen",
"Omri Abend"
] | Probing neural models for the ability to perform downstream tasks using their activation patterns is often used to localize what parts of the network specialize in performing what tasks. However, little work addressed potential mediating factors in such comparisons. As a test-case mediating factor, we consider the pred... | 2021.naacl-main.8 | 10.18653/v1/2021.naacl-main.8 | null | 2104.06400 | title_snapshot |
2021.naacl-main.9 | Automatic Generation of Contrast Sets from Scene Graphs: Probing the Compositional Consistency of GQA | https://aclanthology.org/2021.naacl-main.9/ | [
"Yonatan Bitton",
"Gabriel Stanovsky",
"Roy Schwartz",
"Michael Elhadad"
] | Recent works have shown that supervised models often exploit data artifacts to achieve good test scores while their performance severely degrades on samples outside their training distribution. Contrast sets (Gardneret al., 2020) quantify this phenomenon by perturbing test samples in a minimal way such that the output ... | 2021.naacl-main.9 | 10.18653/v1/2021.naacl-main.9 | null | 2103.09591 | title_snapshot |
2021.naacl-main.10 | Multilingual Language Models Predict Human Reading Behavior | https://aclanthology.org/2021.naacl-main.10/ | [
"Nora Hollenstein",
"Federico Pirovano",
"Ce Zhang",
"Lena Jäger",
"Lisa Beinborn"
] | We analyze if large language models are able to predict patterns of human reading behavior. We compare the performance of language-specific and multilingual pretrained transformer models to predict reading time measures reflecting natural human sentence processing on Dutch, English, German, and Russian texts. This resu... | 2021.naacl-main.10 | 10.18653/v1/2021.naacl-main.10 | null | 2104.05433 | title_snapshot |
2021.naacl-main.11 | Do Syntactic Probes Probe Syntax? Experiments with Jabberwocky Probing | https://aclanthology.org/2021.naacl-main.11/ | [
"Rowan Hall Maudslay",
"Ryan Cotterell"
] | Analysing whether neural language models encode linguistic information has become popular in NLP. One method of doing so, which is frequently cited to support the claim that models like BERT encode syntax, is called probing; probes are small supervised models trained to extract linguistic information from another model... | 2021.naacl-main.11 | 10.18653/v1/2021.naacl-main.11 | null | 2106.02559 | title_snapshot |
2021.naacl-main.12 | A Non-Linear Structural Probe | https://aclanthology.org/2021.naacl-main.12/ | [
"Jennifer C. White",
"Tiago Pimentel",
"Naomi Saphra",
"Ryan Cotterell"
] | Probes are models devised to investigate the encoding of knowledge—e.g. syntactic structure—in contextual representations. Probes are often designed for simplicity, which has led to restrictions on probe design that may not allow for the full exploitation of the structure of encoded information; one such restriction is... | 2021.naacl-main.12 | 10.18653/v1/2021.naacl-main.12 | null | 2105.10185 | title_snapshot |
2021.naacl-main.13 | Concealed Data Poisoning Attacks on NLP Models | https://aclanthology.org/2021.naacl-main.13/ | [
"Eric Wallace",
"Tony Zhao",
"Shi Feng",
"Sameer Singh"
] | Adversarial attacks alter NLP model predictions by perturbing test-time inputs. However, it is much less understood whether, and how, predictions can be manipulated with small, concealed changes to the training data. In this work, we develop a new data poisoning attack that allows an adversary to control model predicti... | 2021.naacl-main.13 | 10.18653/v1/2021.naacl-main.13 | null | 2010.12563 | title_snapshot |
2021.naacl-main.14 | Backtranslation Feedback Improves User Confidence in MT, Not Quality | https://aclanthology.org/2021.naacl-main.14/ | [
"Vilém Zouhar",
"Michal Novák",
"Matúš Žilinec",
"Ondřej Bojar",
"Mateo Obregón",
"Robin L. Hill",
"Frédéric Blain",
"Marina Fomicheva",
"Lucia Specia",
"Lisa Yankovskaya"
] | Translating text into a language unknown to the text’s author, dubbed outbound translation, is a modern need for which the user experience has significant room for improvement, beyond the basic machine translation facility. We demonstrate this by showing three ways in which user confidence in the outbound translation, ... | 2021.naacl-main.14 | 10.18653/v1/2021.naacl-main.14 | null | 2104.05688 | title_snapshot |
2021.naacl-main.15 | Data Filtering using Cross-Lingual Word Embeddings | https://aclanthology.org/2021.naacl-main.15/ | [
"Christian Herold",
"Jan Rosendahl",
"Joris Vanvinckenroye",
"Hermann Ney"
] | Data filtering for machine translation (MT) describes the task of selecting a subset of a given, possibly noisy corpus with the aim to maximize the performance of an MT system trained on this selected data. Over the years, many different filtering approaches have been proposed. However, varying task definitions and dat... | 2021.naacl-main.15 | 10.18653/v1/2021.naacl-main.15 | null | null | null |
2021.naacl-main.16 | Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation | https://aclanthology.org/2021.naacl-main.16/ | [
"Alexandra Chronopoulou",
"Dario Stojanovski",
"Alexander Fraser"
] | Successful methods for unsupervised neural machine translation (UNMT) employ cross-lingual pretraining via self-supervision, often in the form of a masked language modeling or a sequence generation task, which requires the model to align the lexical- and high-level representations of the two languages. While cross-ling... | 2021.naacl-main.16 | 10.18653/v1/2021.naacl-main.16 | null | 2103.10531 | title_snapshot |
2021.naacl-main.17 | Neural Machine Translation without Embeddings | https://aclanthology.org/2021.naacl-main.17/ | [
"Uri Shaham",
"Omer Levy"
] | Many NLP models operate over sequences of subword tokens produced by hand-crafted tokenization rules and heuristic subword induction algorithms. A simple universal alternative is to represent every computerized text as a sequence of bytes via UTF-8, obviating the need for an embedding layer since there are fewer token ... | 2021.naacl-main.17 | 10.18653/v1/2021.naacl-main.17 | null | 2008.09396 | title_snapshot |
2021.naacl-main.18 | Counterfactual Data Augmentation for Neural Machine Translation | https://aclanthology.org/2021.naacl-main.18/ | [
"Qi Liu",
"Matt Kusner",
"Phil Blunsom"
] | We propose a data augmentation method for neural machine translation. It works by interpreting language models and phrasal alignment causally. Specifically, it creates augmented parallel translation corpora by generating (path-specific) counterfactual aligned phrases. We generate these by sampling new source phrases fr... | 2021.naacl-main.18 | 10.18653/v1/2021.naacl-main.18 | null | null | null |
2021.naacl-main.19 | Cultural and Geographical Influences on Image Translatability of Words across Languages | https://aclanthology.org/2021.naacl-main.19/ | [
"Nikzad Khani",
"Isidora Tourni",
"Mohammad Sadegh Rasooli",
"Chris Callison-Burch",
"Derry Tanti Wijaya"
] | Neural Machine Translation (NMT) models have been observed to produce poor translations when there are few/no parallel sentences to train the models. In the absence of parallel data, several approaches have turned to the use of images to learn translations. Since images of words, e.g., horse may be unchanged across lan... | 2021.naacl-main.19 | 10.18653/v1/2021.naacl-main.19 | null | null | null |
2021.naacl-main.20 | Multilingual BERT Post-Pretraining Alignment | https://aclanthology.org/2021.naacl-main.20/ | [
"Lin Pan",
"Chung-Wei Hang",
"Haode Qi",
"Abhishek Shah",
"Saloni Potdar",
"Mo Yu"
] | We propose a simple method to align multilingual contextual embeddings as a post-pretraining step for improved cross-lingual transferability of the pretrained language models. Using parallel data, our method aligns embeddings on the word level through the recently proposed Translation Language Modeling objective as wel... | 2021.naacl-main.20 | 10.18653/v1/2021.naacl-main.20 | null | 2010.12547 | title_snapshot |
2021.naacl-main.21 | A Million Tweets Are Worth a Few Points: Tuning Transformers for Customer Service Tasks | https://aclanthology.org/2021.naacl-main.21/ | [
"Amir Hadifar",
"Sofie Labat",
"Veronique Hoste",
"Chris Develder",
"Thomas Demeester"
] | In online domain-specific customer service applications, many companies struggle to deploy advanced NLP models successfully, due to the limited availability of and noise in their datasets. While prior research demonstrated the potential of migrating large open-domain pretrained models for domain-specific tasks, the app... | 2021.naacl-main.21 | 10.18653/v1/2021.naacl-main.21 | null | 2104.07944 | title_snapshot |
2021.naacl-main.22 | Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases | https://aclanthology.org/2021.naacl-main.22/ | [
"Ilias Chalkidis",
"Manos Fergadiotis",
"Dimitrios Tsarapatsanis",
"Nikolaos Aletras",
"Ion Androutsopoulos",
"Prodromos Malakasiotis"
] | Interpretability or explainability is an emerging research field in NLP. From a user-centric point of view, the goal is to build models that provide proper justification for their decisions, similar to those of humans, by requiring the models to satisfy additional constraints. To this end, we introduce a new applicatio... | 2021.naacl-main.22 | 10.18653/v1/2021.naacl-main.22 | null | 2103.13084 | title_snapshot |
2021.naacl-main.23 | Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products | https://aclanthology.org/2021.naacl-main.23/ | [
"Ohad Rozen",
"David Carmel",
"Avihai Mejer",
"Vitaly Mirkis",
"Yftah Ziser"
] | Predicting the answer to a product-related question is an emerging field of research that recently attracted a lot of attention. Answering subjective and opinion-based questions is most challenging due to the dependency on customer generated content. Previous works mostly focused on review-aware answer prediction; howe... | 2021.naacl-main.23 | 10.18653/v1/2021.naacl-main.23 | null | 2105.08956 | title_snapshot |
2021.naacl-main.24 | EnSidNet: Enhanced Hybrid Siamese-Deep Network for grouping clinical trials into drug-development pathways | https://aclanthology.org/2021.naacl-main.24/ | [
"Lucia Pagani"
] | Siamese Neural Networks have been widely used to perform similarity classification in multi-class settings. Their architecture can be used to group the clinical trials belonging to the same drug-development pathway along the several clinical trial phases. Here we present an approach for the unmet need of drug-developme... | 2021.naacl-main.24 | 10.18653/v1/2021.naacl-main.24 | null | null | null |
2021.naacl-main.25 | DATE: Detecting Anomalies in Text via Self-Supervision of Transformers | https://aclanthology.org/2021.naacl-main.25/ | [
"Andrei Manolache",
"Florin Brad",
"Elena Burceanu"
] | Leveraging deep learning models for Anomaly Detection (AD) has seen widespread use in recent years due to superior performances over traditional methods. Recent deep methods for anomalies in images learn better features of normality in an end-to-end self-supervised setting. These methods train a model to discriminate b... | 2021.naacl-main.25 | 10.18653/v1/2021.naacl-main.25 | null | 2104.05591 | title_snapshot |
2021.naacl-main.26 | A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code | https://aclanthology.org/2021.naacl-main.26/ | [
"Nadezhda Chirkova",
"Sergey Troshin"
] | There is an emerging interest in the application of natural language processing models to source code processing tasks. One of the major problems in applying deep learning to software engineering is that source code often contains a lot of rare identifiers, resulting in huge vocabularies. We propose a simple, yet effec... | 2021.naacl-main.26 | 10.18653/v1/2021.naacl-main.26 | null | 2010.12663 | title_snapshot |
2021.naacl-main.27 | Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition | https://aclanthology.org/2021.naacl-main.27/ | [
"Dingmin Wang",
"Chenghua Lin",
"Qi Liu",
"Kam-Fai Wong"
] | We present a fast and scalable architecture called Explicit Modular Decomposition (EMD), in which we incorporate both classification-based and extraction-based methods and design four modules (for clas- sification and sequence labelling) to jointly extract dialogue states. Experimental results based on the MultiWoz 2.0... | 2021.naacl-main.27 | 10.18653/v1/2021.naacl-main.27 | null | 2004.10663 | title_snapshot |
2021.naacl-main.28 | Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks | https://aclanthology.org/2021.naacl-main.28/ | [
"Nandan Thakur",
"Nils Reimers",
"Johannes Daxenberger",
"Iryna Gurevych"
] | There are two approaches for pairwise sentence scoring: Cross-encoders, which perform full-attention over the input pair, and Bi-encoders, which map each input independently to a dense vector space. While cross-encoders often achieve higher performance, they are too slow for many practical use cases. Bi-encoders, on th... | 2021.naacl-main.28 | 10.18653/v1/2021.naacl-main.28 | null | 2010.08240 | title_snapshot |
2021.naacl-main.29 | SmBoP: Semi-autoregressive Bottom-up Semantic Parsing | https://aclanthology.org/2021.naacl-main.29/ | [
"Ohad Rubin",
"Jonathan Berant"
] | The de-facto standard decoding method for semantic parsing in recent years has been to autoregressively decode the abstract syntax tree of the target program using a top-down depth-first traversal. In this work, we propose an alternative approach: a Semi-autoregressive Bottom-up Parser (SmBoP) that constructs at decodi... | 2021.naacl-main.29 | 10.18653/v1/2021.naacl-main.29 | null | 2010.12412 | title_snapshot |
2021.naacl-main.30 | SGL: Speaking the Graph Languages of Semantic Parsing via Multilingual Translation | https://aclanthology.org/2021.naacl-main.30/ | [
"Luigi Procopio",
"Rocco Tripodi",
"Roberto Navigli"
] | Graph-based semantic parsing aims to represent textual meaning through directed graphs. As one of the most promising general-purpose meaning representations, these structures and their parsing have gained a significant interest momentum during recent years, with several diverse formalisms being proposed. Yet, owing to ... | 2021.naacl-main.30 | 10.18653/v1/2021.naacl-main.30 | null | null | null |
2021.naacl-main.31 | Unifying Cross-Lingual Semantic Role Labeling with Heterogeneous Linguistic Resources | https://aclanthology.org/2021.naacl-main.31/ | [
"Simone Conia",
"Andrea Bacciu",
"Roberto Navigli"
] | While cross-lingual techniques are finding increasing success in a wide range of Natural Language Processing tasks, their application to Semantic Role Labeling (SRL) has been strongly limited by the fact that each language adopts its own linguistic formalism, from PropBank for English to AnCora for Spanish and PDT-Vall... | 2021.naacl-main.31 | 10.18653/v1/2021.naacl-main.31 | Outstanding Long Paper | null | null |
2021.naacl-main.32 | Fool Me Twice: Entailment from Wikipedia Gamification | https://aclanthology.org/2021.naacl-main.32/ | [
"Julian Eisenschlos",
"Bhuwan Dhingra",
"Jannis Bulian",
"Benjamin Börschinger",
"Jordan Boyd-Graber"
] | We release FoolMeTwice (FM2 for short), a large dataset of challenging entailment pairs collected through a fun multi-player game. Gamification encourages adversarial examples, drastically lowering the number of examples that can be solved using “shortcuts” compared to other popular entailment datasets. Players are pre... | 2021.naacl-main.32 | 10.18653/v1/2021.naacl-main.32 | null | 2104.04725 | title_snapshot |
2021.naacl-main.33 | Meta-Learning for Domain Generalization in Semantic Parsing | https://aclanthology.org/2021.naacl-main.33/ | [
"Bailin Wang",
"Mirella Lapata",
"Ivan Titov"
] | The importance of building semantic parsers which can be applied to new domains and generate programs unseen at training has long been acknowledged, and datasets testing out-of-domain performance are becoming increasingly available. However, little or no attention has been devoted to learning algorithms or objectives w... | 2021.naacl-main.33 | 10.18653/v1/2021.naacl-main.33 | null | 2010.11988 | title_snapshot |
2021.naacl-main.34 | Aspect-Controlled Neural Argument Generation | https://aclanthology.org/2021.naacl-main.34/ | [
"Benjamin Schiller",
"Johannes Daxenberger",
"Iryna Gurevych"
] | We rely on arguments in our daily lives to deliver our opinions and base them on evidence, making them more convincing in turn. However, finding and formulating arguments can be challenging. In this work, we present the Arg-CTRL - a language model for argument generation that can be controlled to generate sentence-leve... | 2021.naacl-main.34 | 10.18653/v1/2021.naacl-main.34 | null | 2005.00084 | title_snapshot |
2021.naacl-main.35 | Text Generation from Discourse Representation Structures | https://aclanthology.org/2021.naacl-main.35/ | [
"Jiangming Liu",
"Shay B. Cohen",
"Mirella Lapata"
] | We propose neural models to generate text from formal meaning representations based on Discourse Representation Structures (DRSs). DRSs are document-level representations which encode rich semantic detail pertaining to rhetorical relations, presupposition, and co-reference within and across sentences. We formalize the ... | 2021.naacl-main.35 | 10.18653/v1/2021.naacl-main.35 | null | null | null |
2021.naacl-main.36 | APo-VAE: Text Generation in Hyperbolic Space | https://aclanthology.org/2021.naacl-main.36/ | [
"Shuyang Dai",
"Zhe Gan",
"Yu Cheng",
"Chenyang Tao",
"Lawrence Carin",
"Jingjing Liu"
] | Natural language often exhibits inherent hierarchical structure ingrained with complex syntax and semantics. However, most state-of-the-art deep generative models learn embeddings only in Euclidean vector space, without accounting for this structural property of language. In this paper, we investigate text generation i... | 2021.naacl-main.36 | 10.18653/v1/2021.naacl-main.36 | null | 2005.00054 | title_snapshot |
2021.naacl-main.37 | DART: Open-Domain Structured Data Record to Text Generation | https://aclanthology.org/2021.naacl-main.37/ | [
"Linyong Nan",
"Dragomir Radev",
"Rui Zhang",
"Amrit Rau",
"Abhinand Sivaprasad",
"Chiachun Hsieh",
"Xiangru Tang",
"Aadit Vyas",
"Neha Verma",
"Pranav Krishna",
"Yangxiaokang Liu",
"Nadia Irwanto",
"Jessica Pan",
"Faiaz Rahman",
"Ahmad Zaidi",
"Mutethia Mutuma",
"Yasin Tarabar",
"... | We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures. To this end, we propose a procedure of... | 2021.naacl-main.37 | 10.18653/v1/2021.naacl-main.37 | null | 2007.02871 | title_snapshot |
2021.naacl-main.38 | When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models | https://aclanthology.org/2021.naacl-main.38/ | [
"Benjamin Muller",
"Antonios Anastasopoulos",
"Benoît Sagot",
"Djamé Seddah"
] | Transfer learning based on pretraining language models on a large amount of raw data has become a new norm to reach state-of-the-art performance in NLP. Still, it remains unclear how this approach should be applied for unseen languages that are not covered by any available large-scale multilingual language model and fo... | 2021.naacl-main.38 | 10.18653/v1/2021.naacl-main.38 | null | 2010.12858 | title_snapshot |
2021.naacl-main.39 | Multi-Adversarial Learning for Cross-Lingual Word Embeddings | https://aclanthology.org/2021.naacl-main.39/ | [
"Haozhou Wang",
"James Henderson",
"Paola Merlo"
] | Generative adversarial networks (GANs) have succeeded in inducing cross-lingual word embeddings - maps of matching words across languages - without supervision. Despite these successes, GANs’ performance for the difficult case of distant languages is still not satisfactory. These limitations have been explained by GANs... | 2021.naacl-main.39 | 10.18653/v1/2021.naacl-main.39 | null | 2010.08432 | title_snapshot |
2021.naacl-main.40 | Multi-view Subword Regularization | https://aclanthology.org/2021.naacl-main.40/ | [
"Xinyi Wang",
"Sebastian Ruder",
"Graham Neubig"
] | Multilingual pretrained representations generally rely on subword segmentation algorithms to create a shared multilingual vocabulary. However, standard heuristic algorithms often lead to sub-optimal segmentation, especially for languages with limited amounts of data. In this paper, we take two major steps towards allev... | 2021.naacl-main.40 | 10.18653/v1/2021.naacl-main.40 | null | 2103.08490 | title_snapshot |
2021.naacl-main.41 | mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer | https://aclanthology.org/2021.naacl-main.41/ | [
"Linting Xue",
"Noah Constant",
"Adam Roberts",
"Mihir Kale",
"Rami Al-Rfou",
"Aditya Siddhant",
"Aditya Barua",
"Colin Raffel"
] | The recent “Text-to-Text Transfer Transformer” (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 la... | 2021.naacl-main.41 | 10.18653/v1/2021.naacl-main.41 | null | 2010.11934 | title_snapshot |
2021.naacl-main.42 | MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning | https://aclanthology.org/2021.naacl-main.42/ | [
"Mengzhou Xia",
"Guoqing Zheng",
"Subhabrata Mukherjee",
"Milad Shokouhi",
"Graham Neubig",
"Ahmed Hassan Awadallah"
] | The combination of multilingual pre-trained representations and cross-lingual transfer learning is one of the most effective methods for building functional NLP systems for low-resource languages. However, for extremely low-resource languages without large-scale monolingual corpora for pre-training or sufficient annota... | 2021.naacl-main.42 | 10.18653/v1/2021.naacl-main.42 | null | 2104.07908 | title_snapshot |
2021.naacl-main.43 | Open Domain Question Answering over Tables via Dense Retrieval | https://aclanthology.org/2021.naacl-main.43/ | [
"Jonathan Herzig",
"Thomas Müller",
"Syrine Krichene",
"Julian Eisenschlos"
] | Recent advances in open-domain QA have led to strong models based on dense retrieval, but only focused on retrieving textual passages. In this work, we tackle open-domain QA over tables for the first time, and show that retrieval can be improved by a retriever designed to handle tabular context. We present an effective... | 2021.naacl-main.43 | 10.18653/v1/2021.naacl-main.43 | null | 2103.12011 | title_snapshot |
2021.naacl-main.44 | Open-Domain Question Answering Goes Conversational via Question Rewriting | https://aclanthology.org/2021.naacl-main.44/ | [
"Raviteja Anantha",
"Svitlana Vakulenko",
"Zhucheng Tu",
"Shayne Longpre",
"Stephen Pulman",
"Srinivas Chappidi"
] | We introduce a new dataset for Question Rewriting in Conversational Context (QReCC), which contains 14K conversations with 80K question-answer pairs. The task in QReCC is to find answers to conversational questions within a collection of 10M web pages (split into 54M passages). Answers to questions in the same conversa... | 2021.naacl-main.44 | 10.18653/v1/2021.naacl-main.44 | null | 2010.04898 | title_snapshot |
2021.naacl-main.45 | QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering | https://aclanthology.org/2021.naacl-main.45/ | [
"Michihiro Yasunaga",
"Hongyu Ren",
"Antoine Bosselut",
"Percy Liang",
"Jure Leskovec"
] | The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG. Here... | 2021.naacl-main.45 | 10.18653/v1/2021.naacl-main.45 | null | 2104.06378 | title_snapshot |
2021.naacl-main.46 | XOR QA: Cross-lingual Open-Retrieval Question Answering | https://aclanthology.org/2021.naacl-main.46/ | [
"Akari Asai",
"Jungo Kasai",
"Jonathan Clark",
"Kenton Lee",
"Eunsol Choi",
"Hannaneh Hajishirzi"
] | Multilingual question answering tasks typically assume that answers exist in the same language as the question. Yet in practice, many languages face both information scarcity—where languages have few reference articles—and information asymmetry—where questions reference concepts from other cultures. This work extends o... | 2021.naacl-main.46 | 10.18653/v1/2021.naacl-main.46 | null | 2010.11856 | title_snapshot |
2021.naacl-main.47 | SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval | https://aclanthology.org/2021.naacl-main.47/ | [
"Tiancheng Zhao",
"Xiaopeng Lu",
"Kyusong Lee"
] | We introduce SPARTA, a novel neural retrieval method that shows great promise in performance, generalization, and interpretability for open-domain question answering. Unlike many neural ranking methods that use dense vector nearest neighbor search, SPARTA learns a sparse representation that can be efficiently implement... | 2021.naacl-main.47 | 10.18653/v1/2021.naacl-main.47 | null | 2009.13013 | title_snapshot |
2021.naacl-main.48 | Implicitly Abusive Language – What does it actually look like and why are we not getting there? | https://aclanthology.org/2021.naacl-main.48/ | [
"Michael Wiegand",
"Josef Ruppenhofer",
"Elisabeth Eder"
] | Abusive language detection is an emerging field in natural language processing which has received a large amount of attention recently. Still the success of automatic detection is limited. Particularly, the detection of implicitly abusive language, i.e. abusive language that is not conveyed by abusive words (e.g. dumba... | 2021.naacl-main.48 | 10.18653/v1/2021.naacl-main.48 | null | null | null |
2021.naacl-main.49 | The Importance of Modeling Social Factors of Language: Theory and Practice | https://aclanthology.org/2021.naacl-main.49/ | [
"Dirk Hovy",
"Diyi Yang"
] | Natural language processing (NLP) applications are now more powerful and ubiquitous than ever before. With rapidly developing (neural) models and ever-more available data, current NLP models have access to more information than any human speaker during their life. Still, it would be hard to argue that NLP models have r... | 2021.naacl-main.49 | 10.18653/v1/2021.naacl-main.49 | null | null | null |
2021.naacl-main.50 | On learning and representing social meaning in NLP: a sociolinguistic perspective | https://aclanthology.org/2021.naacl-main.50/ | [
"Dong Nguyen",
"Laura Rosseel",
"Jack Grieve"
] | The field of NLP has made substantial progress in building meaning representations. However, an important aspect of linguistic meaning, social meaning, has been largely overlooked. We introduce the concept of social meaning to NLP and discuss how insights from sociolinguistics can inform work on representation learning... | 2021.naacl-main.50 | 10.18653/v1/2021.naacl-main.50 | null | null | null |
2021.naacl-main.51 | Preregistering NLP research | https://aclanthology.org/2021.naacl-main.51/ | [
"Emiel van Miltenburg",
"Chris van der Lee",
"Emiel Krahmer"
] | Preregistration refers to the practice of specifying what you are going to do, and what you expect to find in your study, before carrying out the study. This practice is increasingly common in medicine and psychology, but is rarely discussed in NLP. This paper discusses preregistration in more detail, explores how NLP ... | 2021.naacl-main.51 | 10.18653/v1/2021.naacl-main.51 | Best Thematic Paper | 2103.06944 | title_snapshot |
2021.naacl-main.52 | Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence | https://aclanthology.org/2021.naacl-main.52/ | [
"Tal Schuster",
"Adam Fisch",
"Regina Barzilay"
] | Typical fact verification models use retrieved written evidence to verify claims. Evidence sources, however, often change over time as more information is gathered and revised. In order to adapt, models must be sensitive to subtle differences in supporting evidence. We present VitaminC, a benchmark infused with challen... | 2021.naacl-main.52 | 10.18653/v1/2021.naacl-main.52 | null | 2103.08541 | title_snapshot |
2021.naacl-main.53 | Representing Numbers in NLP: a Survey and a Vision | https://aclanthology.org/2021.naacl-main.53/ | [
"Avijit Thawani",
"Jay Pujara",
"Filip Ilievski",
"Pedro Szekely"
] | NLP systems rarely give special consideration to numbers found in text. This starkly contrasts with the consensus in neuroscience that, in the brain, numbers are represented differently from words. We arrange recent NLP work on numeracy into a comprehensive taxonomy of tasks and methods. We break down the subjective no... | 2021.naacl-main.53 | 10.18653/v1/2021.naacl-main.53 | null | 2103.13136 | title_snapshot |
2021.naacl-main.54 | Extending Multi-Document Summarization Evaluation to the Interactive Setting | https://aclanthology.org/2021.naacl-main.54/ | [
"Ori Shapira",
"Ramakanth Pasunuru",
"Hadar Ronen",
"Mohit Bansal",
"Yael Amsterdamer",
"Ido Dagan"
] | Allowing users to interact with multi-document summarizers is a promising direction towards improving and customizing summary results. Different ideas for interactive summarization have been proposed in previous work but these solutions are highly divergent and incomparable. In this paper, we develop an end-to-end eval... | 2021.naacl-main.54 | 10.18653/v1/2021.naacl-main.54 | null | null | null |
2021.naacl-main.55 | Identifying Helpful Sentences in Product Reviews | https://aclanthology.org/2021.naacl-main.55/ | [
"Iftah Gamzu",
"Hila Gonen",
"Gilad Kutiel",
"Ran Levy",
"Eugene Agichtein"
] | In recent years online shopping has gained momentum and became an important venue for customers wishing to save time and simplify their shopping process. A key advantage of shopping online is the ability to read what other customers are saying about products of interest. In this work, we aim to maintain this advantage ... | 2021.naacl-main.55 | 10.18653/v1/2021.naacl-main.55 | null | 2104.09792 | title_snapshot |
2021.naacl-main.56 | Noisy Self-Knowledge Distillation for Text Summarization | https://aclanthology.org/2021.naacl-main.56/ | [
"Yang Liu",
"Sheng Shen",
"Mirella Lapata"
] | In this paper we apply self-knowledge distillation to text summarization which we argue can alleviate problems with maximum-likelihood training on single reference and noisy datasets. Instead of relying on one-hot annotation labels, our student summarization model is trained with guidance from a teacher which generates... | 2021.naacl-main.56 | 10.18653/v1/2021.naacl-main.56 | null | 2009.07032 | title_snapshot |
2021.naacl-main.57 | Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation | https://aclanthology.org/2021.naacl-main.57/ | [
"Alexander Fabbri",
"Simeng Han",
"Haoyuan Li",
"Haoran Li",
"Marjan Ghazvininejad",
"Shafiq Joty",
"Dragomir Radev",
"Yashar Mehdad"
] | Models pretrained with self-supervised objectives on large text corpora achieve state-of-the-art performance on English text summarization tasks. However, these models are typically fine-tuned on hundreds of thousands of data points, an infeasible requirement when applying summarization to new, niche domains. In this w... | 2021.naacl-main.57 | 10.18653/v1/2021.naacl-main.57 | null | 2010.12836 | title_snapshot |
2021.naacl-main.58 | Enhancing Factual Consistency of Abstractive Summarization | https://aclanthology.org/2021.naacl-main.58/ | [
"Chenguang Zhu",
"William Hinthorn",
"Ruochen Xu",
"Qingkai Zeng",
"Michael Zeng",
"Xuedong Huang",
"Meng Jiang"
] | Automatic abstractive summaries are found to often distort or fabricate facts in the article. This inconsistency between summary and original text has seriously impacted its applicability. We propose a fact-aware summarization model FASum to extract and integrate factual relations into the summary generation process vi... | 2021.naacl-main.58 | 10.18653/v1/2021.naacl-main.58 | null | 2003.08612 | title_snapshot |
2021.naacl-main.59 | Few-shot Intent Classification and Slot Filling with Retrieved Examples | https://aclanthology.org/2021.naacl-main.59/ | [
"Dian Yu",
"Luheng He",
"Yuan Zhang",
"Xinya Du",
"Panupong Pasupat",
"Qi Li"
] | Few-shot learning arises in important practical scenarios, such as when a natural language understanding system needs to learn new semantic labels for an emerging, resource-scarce domain. In this paper, we explore retrieval-based methods for intent classification and slot filling tasks in few-shot settings. Retrieval-b... | 2021.naacl-main.59 | 10.18653/v1/2021.naacl-main.59 | null | 2104.05763 | title_snapshot |
2021.naacl-main.60 | “Nice Try, Kiddo”: Investigating Ad Hominems in Dialogue Responses | https://aclanthology.org/2021.naacl-main.60/ | [
"Emily Sheng",
"Kai-Wei Chang",
"Prem Natarajan",
"Nanyun Peng"
] | Ad hominem attacks are those that target some feature of a person’s character instead of the position the person is maintaining. These attacks are harmful because they propagate implicit biases and diminish a person’s credibility. Since dialogue systems respond directly to user input, it is important to study ad homine... | 2021.naacl-main.60 | 10.18653/v1/2021.naacl-main.60 | null | 2010.12820 | title_snapshot |
2021.naacl-main.61 | Human-like informative conversations: Better acknowledgements using conditional mutual information | https://aclanthology.org/2021.naacl-main.61/ | [
"Ashwin Paranjape",
"Christopher Manning"
] | This work aims to build a dialogue agent that can weave new factual content into conversations as naturally as humans. We draw insights from linguistic principles of conversational analysis and annotate human-human conversations from the Switchboard Dialog Act Corpus to examine humans strategies for acknowledgement, tr... | 2021.naacl-main.61 | 10.18653/v1/2021.naacl-main.61 | null | 2104.07831 | title_snapshot |
2021.naacl-main.62 | A Comparative Study on Schema-Guided Dialogue State Tracking | https://aclanthology.org/2021.naacl-main.62/ | [
"Jie Cao",
"Yi Zhang"
] | Frame-based state representation is widely used in modern task-oriented dialog systems to model user intentions and slot values. However, a fixed design of domain ontology makes it difficult to extend to new services and APIs. Recent work proposed to use natural language descriptions to define the domain ontology inste... | 2021.naacl-main.62 | 10.18653/v1/2021.naacl-main.62 | null | null | null |
2021.naacl-main.63 | Spoken Language Understanding for Task-oriented Dialogue Systems with Augmented Memory Networks | https://aclanthology.org/2021.naacl-main.63/ | [
"Jie Wu",
"Ian Harris",
"Hongzhi Zhao"
] | Spoken language understanding, usually including intent detection and slot filling, is a core component to build a spoken dialog system. Recent research shows promising results by jointly learning of those two tasks based on the fact that slot filling and intent detection are sharing semantic knowledge. Furthermore, at... | 2021.naacl-main.63 | 10.18653/v1/2021.naacl-main.63 | null | null | null |
2021.naacl-main.64 | How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds | https://aclanthology.org/2021.naacl-main.64/ | [
"Prithviraj Ammanabrolu",
"Jack Urbanek",
"Margaret Li",
"Arthur Szlam",
"Tim Rocktäschel",
"Jason Weston"
] | We seek to create agents that both act and communicate with other agents in pursuit of a goal. Towards this end, we extend LIGHT (Urbanek et al. 2019)—a large-scale crowd-sourced fantasy text-game—with a dataset of quests. These contain natural language motivations paired with in-game goals and human demonstrations; co... | 2021.naacl-main.64 | 10.18653/v1/2021.naacl-main.64 | null | 2010.00685 | title_snapshot |
2021.naacl-main.65 | Linking Entities to Unseen Knowledge Bases with Arbitrary Schemas | https://aclanthology.org/2021.naacl-main.65/ | [
"Yogarshi Vyas",
"Miguel Ballesteros"
] | In entity linking, mentions of named entities in raw text are disambiguated against a knowledge base (KB). This work focuses on linking to unseen KBs that do not have training data and whose schema is unknown during training. Our approach relies on methods to flexibly convert entities with several attribute-value pairs... | 2021.naacl-main.65 | 10.18653/v1/2021.naacl-main.65 | null | 2010.11333 | title_snapshot |
2021.naacl-main.66 | Self-Training with Weak Supervision | https://aclanthology.org/2021.naacl-main.66/ | [
"Giannis Karamanolakis",
"Subhabrata Mukherjee",
"Guoqing Zheng",
"Ahmed Hassan Awadallah"
] | State-of-the-art deep neural networks require large-scale labeled training data that is often expensive to obtain or not available for many tasks. Weak supervision in the form of domain-specific rules has been shown to be useful in such settings to automatically generate weakly labeled training data. However, learning ... | 2021.naacl-main.66 | 10.18653/v1/2021.naacl-main.66 | null | 2104.05514 | title_snapshot |
2021.naacl-main.67 | Neural Language Modeling for Contextualized Temporal Graph Generation | https://aclanthology.org/2021.naacl-main.67/ | [
"Aman Madaan",
"Yiming Yang"
] | This paper presents the first study on using large-scale pre-trained language models for automated generation of an event-level temporal graph for a document. Despite the huge success of neural pre-training methods in NLP tasks, its potential for temporal reasoning over event graphs has not been sufficiently explored. ... | 2021.naacl-main.67 | 10.18653/v1/2021.naacl-main.67 | null | 2010.10077 | title_snapshot |
2021.naacl-main.68 | Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning | https://aclanthology.org/2021.naacl-main.68/ | [
"Xuelu Chen",
"Michael Boratko",
"Muhao Chen",
"Shib Sankar Dasgupta",
"Xiang Lorraine Li",
"Andrew McCallum"
] | Knowledge bases often consist of facts which are harvested from a variety of sources, many of which are noisy and some of which conflict, resulting in a level of uncertainty for each triple. Knowledge bases are also often incomplete, prompting the use of embedding methods to generalize from known facts, however, existi... | 2021.naacl-main.68 | 10.18653/v1/2021.naacl-main.68 | null | 2104.04597 | title_snapshot |
2021.naacl-main.69 | Document-Level Event Argument Extraction by Conditional Generation | https://aclanthology.org/2021.naacl-main.69/ | [
"Sha Li",
"Heng Ji",
"Jiawei Han"
] | Event extraction has long been treated as a sentence-level task in the IE community. We argue that this setting does not match human informative seeking behavior and leads to incomplete and uninformative extraction results. We propose a document-level neural event argument extraction model by formulating the task as co... | 2021.naacl-main.69 | 10.18653/v1/2021.naacl-main.69 | null | 2104.05919 | title_snapshot |
2021.naacl-main.70 | Template Filling with Generative Transformers | https://aclanthology.org/2021.naacl-main.70/ | [
"Xinya Du",
"Alexander Rush",
"Claire Cardie"
] | Template filling is generally tackled by a pipeline of two separate supervised systems – one for role-filler extraction and another for template/event recognition. Since pipelines consider events in isolation, they can suffer from error propagation. We introduce a framework based on end-to-end generative transformers f... | 2021.naacl-main.70 | 10.18653/v1/2021.naacl-main.70 | null | null | null |
2021.naacl-main.71 | Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU models | https://aclanthology.org/2021.naacl-main.71/ | [
"Mengnan Du",
"Varun Manjunatha",
"Rajiv Jain",
"Ruchi Deshpande",
"Franck Dernoncourt",
"Jiuxiang Gu",
"Tong Sun",
"Xia Hu"
] | Recent studies indicate that NLU models are prone to rely on shortcut features for prediction, without achieving true language understanding. As a result, these models fail to generalize to real-world out-of-distribution data. In this work, we show that the words in the NLU training set can be modeled as a long-tailed ... | 2021.naacl-main.71 | 10.18653/v1/2021.naacl-main.71 | null | 2103.06922 | title_snapshot |
2021.naacl-main.72 | On Attention Redundancy: A Comprehensive Study | https://aclanthology.org/2021.naacl-main.72/ | [
"Yuchen Bian",
"Jiaji Huang",
"Xingyu Cai",
"Jiahong Yuan",
"Kenneth Church"
] | Multi-layer multi-head self-attention mechanism is widely applied in modern neural language models. Attention redundancy has been observed among attention heads but has not been deeply studied in the literature. Using BERT-base model as an example, this paper provides a comprehensive study on attention redundancy which... | 2021.naacl-main.72 | 10.18653/v1/2021.naacl-main.72 | null | null | null |
2021.naacl-main.73 | Does BERT Pretrained on Clinical Notes Reveal Sensitive Data? | https://aclanthology.org/2021.naacl-main.73/ | [
"Eric Lehman",
"Sarthak Jain",
"Karl Pichotta",
"Yoav Goldberg",
"Byron C. Wallace"
] | Large Transformers pretrained over clinical notes from Electronic Health Records (EHR) have afforded substantial gains in performance on predictive clinical tasks. The cost of training such models (and the necessity of data access to do so) coupled with their utility motivates parameter sharing, i.e., the release of pr... | 2021.naacl-main.73 | 10.18653/v1/2021.naacl-main.73 | null | 2104.07762 | title_snapshot |
2021.naacl-main.74 | Low-Complexity Probing via Finding Subnetworks | https://aclanthology.org/2021.naacl-main.74/ | [
"Steven Cao",
"Victor Sanh",
"Alexander Rush"
] | The dominant approach in probing neural networks for linguistic properties is to train a new shallow multi-layer perceptron (MLP) on top of the model’s internal representations. This approach can detect properties encoded in the model, but at the cost of adding new parameters that may learn the task directly. We instea... | 2021.naacl-main.74 | 10.18653/v1/2021.naacl-main.74 | null | 2104.03514 | title_snapshot |
2021.naacl-main.75 | An Empirical Comparison of Instance Attribution Methods for NLP | https://aclanthology.org/2021.naacl-main.75/ | [
"Pouya Pezeshkpour",
"Sarthak Jain",
"Byron Wallace",
"Sameer Singh"
] | Widespread adoption of deep models has motivated a pressing need for approaches to interpret network outputs and to facilitate model debugging. Instance attribution methods constitute one means of accomplishing these goals by retrieving training instances that (may have) led to a particular prediction. Influence functi... | 2021.naacl-main.75 | 10.18653/v1/2021.naacl-main.75 | null | 2104.04128 | title_snapshot |
2021.naacl-main.76 | Generalization in Instruction Following Systems | https://aclanthology.org/2021.naacl-main.76/ | [
"Soham Dan",
"Michael Zhou",
"Dan Roth"
] | Understanding and executing natural language instructions in a grounded domain is one of the hallmarks of artificial intelligence. In this paper, we focus on instruction understanding in the blocks world domain and investigate the language understanding abilities of two top-performing systems for the task. We aim to un... | 2021.naacl-main.76 | 10.18653/v1/2021.naacl-main.76 | null | null | null |
2021.naacl-main.77 | LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval | https://aclanthology.org/2021.naacl-main.77/ | [
"Siqi Sun",
"Yen-Chun Chen",
"Linjie Li",
"Shuohang Wang",
"Yuwei Fang",
"Jingjing Liu"
] | Multimodal pre-training has propelled great advancement in vision-and-language research. These large-scale pre-trained models, although successful, fatefully suffer from slow inference speed due to enormous computational cost mainly from cross-modal attention in Transformer architecture. When applied to real-life appli... | 2021.naacl-main.77 | 10.18653/v1/2021.naacl-main.77 | null | 2103.08784 | title_snapshot |
2021.naacl-main.78 | Measuring Social Biases in Grounded Vision and Language Embeddings | https://aclanthology.org/2021.naacl-main.78/ | [
"Candace Ross",
"Boris Katz",
"Andrei Barbu"
] | We generalize the notion of measuring social biases in word embeddings to visually grounded word embeddings. Biases are present in grounded embeddings, and indeed seem to be equally or more significant than for ungrounded embeddings. This is despite the fact that vision and language can suffer from different biases, wh... | 2021.naacl-main.78 | 10.18653/v1/2021.naacl-main.78 | null | 2002.08911 | title_snapshot |
2021.naacl-main.79 | MTAG: Modal-Temporal Attention Graph for Unaligned Human Multimodal Language Sequences | https://aclanthology.org/2021.naacl-main.79/ | [
"Jianing Yang",
"Yongxin Wang",
"Ruitao Yi",
"Yuying Zhu",
"Azaan Rehman",
"Amir Zadeh",
"Soujanya Poria",
"Louis-Philippe Morency"
] | Human communication is multimodal in nature; it is through multiple modalities such as language, voice, and facial expressions, that opinions and emotions are expressed. Data in this domain exhibits complex multi-relational and temporal interactions. Learning from this data is a fundamentally challenging research probl... | 2021.naacl-main.79 | 10.18653/v1/2021.naacl-main.79 | null | 2010.11985 | title_snapshot |
2021.naacl-main.80 | Grounding Open-Domain Instructions to Automate Web Support Tasks | https://aclanthology.org/2021.naacl-main.80/ | [
"Nancy Xu",
"Sam Masling",
"Michael Du",
"Giovanni Campagna",
"Larry Heck",
"James Landay",
"Monica Lam"
] | Grounding natural language instructions on the web to perform previously unseen tasks enables accessibility and automation. We introduce a task and dataset to train AI agents from open-domain, step-by-step instructions originally written for people. We build RUSS (Rapid Universal Support Service) to tackle this problem... | 2021.naacl-main.80 | 10.18653/v1/2021.naacl-main.80 | null | 2103.16057 | title_snapshot |
2021.naacl-main.81 | Modular Networks for Compositional Instruction Following | https://aclanthology.org/2021.naacl-main.81/ | [
"Rodolfo Corona",
"Daniel Fried",
"Coline Devin",
"Dan Klein",
"Trevor Darrell"
] | Standard architectures used in instruction following often struggle on novel compositions of subgoals (e.g. navigating to landmarks or picking up objects) observed during training. We propose a modular architecture for following natural language instructions that describe sequences of diverse subgoals. In our approach,... | 2021.naacl-main.81 | 10.18653/v1/2021.naacl-main.81 | null | 2010.12764 | title_snapshot |
2021.naacl-main.82 | Improving Cross-Modal Alignment in Vision Language Navigation via Syntactic Information | https://aclanthology.org/2021.naacl-main.82/ | [
"Jialu Li",
"Hao Tan",
"Mohit Bansal"
] | Vision language navigation is the task that requires an agent to navigate through a 3D environment based on natural language instructions. One key challenge in this task is to ground instructions with the current visual information that the agent perceives. Most of the existing work employs soft attention over individu... | 2021.naacl-main.82 | 10.18653/v1/2021.naacl-main.82 | null | 2104.09580 | title_snapshot |
2021.naacl-main.83 | Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoning | https://aclanthology.org/2021.naacl-main.83/ | [
"Hui Liu",
"Danqing Zhang",
"Bing Yin",
"Xiaodan Zhu"
] | Exploiting label hierarchies has become a promising approach to tackling the zero-shot multi-label text classification (ZS-MTC) problem. Conventional methods aim to learn a matching model between text and labels, using a graph encoder to incorporate label hierarchies to obtain effective label representations (Rios and ... | 2021.naacl-main.83 | 10.18653/v1/2021.naacl-main.83 | null | 2104.01666 | title_snapshot |
2021.naacl-main.84 | Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach | https://aclanthology.org/2021.naacl-main.84/ | [
"Yue Yu",
"Simiao Zuo",
"Haoming Jiang",
"Wendi Ren",
"Tuo Zhao",
"Chao Zhang"
] | Fine-tuned pre-trained language models (LMs) have achieved enormous success in many natural language processing (NLP) tasks, but they still require excessive labeled data in the fine-tuning stage. We study the problem of fine-tuning pre-trained LMs using only weak supervision, without any labeled data. This problem is ... | 2021.naacl-main.84 | 10.18653/v1/2021.naacl-main.84 | null | 2010.07835 | title_snapshot |
2021.naacl-main.85 | Posterior Differential Regularization with f-divergence for Improving Model Robustness | https://aclanthology.org/2021.naacl-main.85/ | [
"Hao Cheng",
"Xiaodong Liu",
"Lis Pereira",
"Yaoliang Yu",
"Jianfeng Gao"
] | We address the problem of enhancing model robustness through regularization. Specifically, we focus on methods that regularize the model posterior difference between clean and noisy inputs. Theoretically, we provide a connection of two recent methods, Jacobian Regularization and Virtual Adversarial Training, under this... | 2021.naacl-main.85 | 10.18653/v1/2021.naacl-main.85 | null | 2010.12638 | title_snapshot |
2021.naacl-main.86 | Understanding Hard Negatives in Noise Contrastive Estimation | https://aclanthology.org/2021.naacl-main.86/ | [
"Wenzheng Zhang",
"Karl Stratos"
] | The choice of negative examples is important in noise contrastive estimation. Recent works find that hard negatives—highest-scoring incorrect examples under the model—are effective in practice, but they are used without a formal justification. We develop analytical tools to understand the role of hard negatives. Specif... | 2021.naacl-main.86 | 10.18653/v1/2021.naacl-main.86 | null | 2104.06245 | title_snapshot |
2021.naacl-main.87 | Certified Robustness to Word Substitution Attack with Differential Privacy | https://aclanthology.org/2021.naacl-main.87/ | [
"Wenjie Wang",
"Pengfei Tang",
"Jian Lou",
"Li Xiong"
] | The robustness and security of natural language processing (NLP) models are significantly important in real-world applications. In the context of text classification tasks, adversarial examples can be designed by substituting words with synonyms under certain semantic and syntactic constraints, such that a well-trained... | 2021.naacl-main.87 | 10.18653/v1/2021.naacl-main.87 | null | null | null |
2021.naacl-main.88 | DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference | https://aclanthology.org/2021.naacl-main.88/ | [
"Shikhar Murty",
"Tatsunori B. Hashimoto",
"Christopher Manning"
] | Meta-learning promises few-shot learners that can adapt to new distributions by repurposing knowledge acquired from previous training. However, we believe meta-learning has not yet succeeded in NLP due to the lack of a well-defined task distribution, leading to attempts that treat datasets as tasks. Such an ad hoc task... | 2021.naacl-main.88 | 10.18653/v1/2021.naacl-main.88 | null | null | null |
2021.naacl-main.89 | Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languages | https://aclanthology.org/2021.naacl-main.89/ | [
"Xavier Garcia",
"Aditya Siddhant",
"Orhan Firat",
"Ankur Parikh"
] | Unsupervised translation has reached impressive performance on resource-rich language pairs such as English-French and English-German. However, early studies have shown that in more realistic settings involving low-resource, rare languages, unsupervised translation performs poorly, achieving less than 3.0 BLEU. In this... | 2021.naacl-main.89 | 10.18653/v1/2021.naacl-main.89 | null | 2009.11201 | title_snapshot |
2021.naacl-main.90 | Macro-Average: Rare Types Are Important Too | https://aclanthology.org/2021.naacl-main.90/ | [
"Thamme Gowda",
"Weiqiu You",
"Constantine Lignos",
"Jonathan May"
] | While traditional corpus-level evaluation metrics for machine translation (MT) correlate well with fluency, they struggle to reflect adequacy. Model-based MT metrics trained on segment-level human judgments have emerged as an attractive replacement due to strong correlation results. These models, however, require poten... | 2021.naacl-main.90 | 10.18653/v1/2021.naacl-main.90 | null | 2104.05700 | title_snapshot |
2021.naacl-main.91 | Assessing Reference-Free Peer Evaluation for Machine Translation | https://aclanthology.org/2021.naacl-main.91/ | [
"Sweta Agrawal",
"George Foster",
"Markus Freitag",
"Colin Cherry"
] | Reference-free evaluation has the potential to make machine translation evaluation substantially more scalable, allowing us to pivot easily to new languages or domains. It has been recently shown that the probabilities given by a large, multilingual model can achieve state of the art results when used as a reference-fr... | 2021.naacl-main.91 | 10.18653/v1/2021.naacl-main.91 | null | 2104.05146 | title_snapshot |
2021.naacl-main.92 | The Curious Case of Hallucinations in Neural Machine Translation | https://aclanthology.org/2021.naacl-main.92/ | [
"Vikas Raunak",
"Arul Menezes",
"Marcin Junczys-Dowmunt"
] | In this work, we study hallucinations in Neural Machine Translation (NMT), which lie at an extreme end on the spectrum of NMT pathologies. Firstly, we connect the phenomenon of hallucinations under source perturbation to the Long-Tail theory of Feldman, and present an empirically validated hypothesis that explains hall... | 2021.naacl-main.92 | 10.18653/v1/2021.naacl-main.92 | null | 2104.06683 | title_snapshot |
2021.naacl-main.93 | Towards Continual Learning for Multilingual Machine Translation via Vocabulary Substitution | https://aclanthology.org/2021.naacl-main.93/ | [
"Xavier Garcia",
"Noah Constant",
"Ankur Parikh",
"Orhan Firat"
] | We propose a straightforward vocabulary adaptation scheme to extend the language capacity of multilingual machine translation models, paving the way towards efficient continual learning for multilingual machine translation. Our approach is suitable for large-scale datasets, applies to distant languages with unseen scri... | 2021.naacl-main.93 | 10.18653/v1/2021.naacl-main.93 | null | 2103.06799 | title_snapshot |
2021.naacl-main.94 | Towards Modeling the Style of Translators in Neural Machine Translation | https://aclanthology.org/2021.naacl-main.94/ | [
"Yue Wang",
"Cuong Hoang",
"Marcello Federico"
] | One key ingredient of neural machine translation is the use of large datasets from different domains and resources (e.g. Europarl, TED talks). These datasets contain documents translated by professional translators using different but consistent translation styles. Despite that, the model is usually trained in a way th... | 2021.naacl-main.94 | 10.18653/v1/2021.naacl-main.94 | null | null | null |
2021.naacl-main.95 | Self-Supervised Test-Time Learning for Reading Comprehension | https://aclanthology.org/2021.naacl-main.95/ | [
"Pratyay Banerjee",
"Tejas Gokhale",
"Chitta Baral"
] | Recent work on unsupervised question answering has shown that models can be trained with procedurally generated question-answer pairs and can achieve performance competitive with supervised methods. In this work, we consider the task of unsupervised reading comprehension and present a method that performs “test-time le... | 2021.naacl-main.95 | 10.18653/v1/2021.naacl-main.95 | null | 2103.11263 | title_snapshot |
2021.naacl-main.96 | Capturing Row and Column Semantics in Transformer Based Question Answering over Tables | https://aclanthology.org/2021.naacl-main.96/ | [
"Michael Glass",
"Mustafa Canim",
"Alfio Gliozzo",
"Saneem Chemmengath",
"Vishwajeet Kumar",
"Rishav Chakravarti",
"Avi Sil",
"Feifei Pan",
"Samarth Bharadwaj",
"Nicolas Rodolfo Fauceglia"
] | Transformer based architectures are recently used for the task of answering questions over tables. In order to improve the accuracy on this task, specialized pre-training techniques have been developed and applied on millions of open-domain web tables. In this paper, we propose two novel approaches demonstrating that o... | 2021.naacl-main.96 | 10.18653/v1/2021.naacl-main.96 | null | 2104.08303 | title_snapshot |
2021.naacl-main.97 | Explainable Multi-hop Verbal Reasoning Through Internal Monologue | https://aclanthology.org/2021.naacl-main.97/ | [
"Zhengzhong Liang",
"Steven Bethard",
"Mihai Surdeanu"
] | Many state-of-the-art (SOTA) language models have achieved high accuracy on several multi-hop reasoning problems. However, these approaches tend to not be interpretable because they do not make the intermediate reasoning steps explicit. Moreover, models trained on simpler tasks tend to fail when directly tested on more... | 2021.naacl-main.97 | 10.18653/v1/2021.naacl-main.97 | null | null | null |
2021.naacl-main.98 | Robust Question Answering Through Sub-part Alignment | https://aclanthology.org/2021.naacl-main.98/ | [
"Jifan Chen",
"Greg Durrett"
] | Current textual question answering (QA) models achieve strong performance on in-domain test sets, but often do so by fitting surface-level patterns, so they fail to generalize to out-of-distribution settings. To make a more robust and understandable QA system, we model question answering as an alignment problem. We dec... | 2021.naacl-main.98 | 10.18653/v1/2021.naacl-main.98 | null | 2004.14648 | title_snapshot |
2021.naacl-main.99 | Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models | https://aclanthology.org/2021.naacl-main.99/ | [
"Tushar Khot",
"Daniel Khashabi",
"Kyle Richardson",
"Peter Clark",
"Ashish Sabharwal"
] | We propose a general framework called Text Modular Networks(TMNs) for building interpretable systems that learn to solve complex tasks by decomposing them into simpler ones solvable by existing models. To ensure solvability of simpler tasks, TMNs learn the textual input-output behavior (i.e., language) of existing mode... | 2021.naacl-main.99 | 10.18653/v1/2021.naacl-main.99 | null | 2009.00751 | title_snapshot |
2021.naacl-main.100 | RECONSIDER: Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering | https://aclanthology.org/2021.naacl-main.100/ | [
"Srinivasan Iyer",
"Sewon Min",
"Yashar Mehdad",
"Wen-tau Yih"
] | State-of-the-art Machine Reading Comprehension (MRC) models for Open-domain Question Answering (QA) are typically trained for span selection using distantly supervised positive examples and heuristically retrieved negative examples. This training scheme possibly explains empirical observations that these models achieve... | 2021.naacl-main.100 | 10.18653/v1/2021.naacl-main.100 | null | 2010.10757 | title_judge |
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