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.101 | On the Transferability of Minimal Prediction Preserving Inputs in Question Answering | https://aclanthology.org/2021.naacl-main.101/ | [
"Shayne Longpre",
"Yi Lu",
"Chris DuBois"
] | Recent work (Feng et al., 2018) establishes the presence of short, uninterpretable input fragments that yield high confidence and accuracy in neural models. We refer to these as Minimal Prediction Preserving Inputs (MPPIs). In the context of question answering, we investigate competing hypotheses for the existence of M... | 2021.naacl-main.101 | 10.18653/v1/2021.naacl-main.101 | null | 2009.08070 | title_snapshot |
2021.naacl-main.102 | Understanding by Understanding Not: Modeling Negation in Language Models | https://aclanthology.org/2021.naacl-main.102/ | [
"Arian Hosseini",
"Siva Reddy",
"Dzmitry Bahdanau",
"R Devon Hjelm",
"Alessandro Sordoni",
"Aaron Courville"
] | Negation is a core construction in natural language. Despite being very successful on many tasks, state-of-the-art pre-trained language models often handle negation incorrectly. To improve language models in this regard, we propose to augment the language modeling objective with an unlikelihood objective that is based ... | 2021.naacl-main.102 | 10.18653/v1/2021.naacl-main.102 | null | 2105.03519 | title_snapshot |
2021.naacl-main.103 | DuoRAT: Towards Simpler Text-to-SQL Models | https://aclanthology.org/2021.naacl-main.103/ | [
"Torsten Scholak",
"Raymond Li",
"Dzmitry Bahdanau",
"Harm de Vries",
"Chris Pal"
] | Recent neural text-to-SQL models can effectively translate natural language questions to corresponding SQL queries on unseen databases. Working mostly on the Spider dataset, researchers have proposed increasingly sophisticated solutions to the problem. Contrary to this trend, in this paper we focus on simplifications. ... | 2021.naacl-main.103 | 10.18653/v1/2021.naacl-main.103 | null | 2010.11119 | title_snapshot |
2021.naacl-main.104 | Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization | https://aclanthology.org/2021.naacl-main.104/ | [
"Anshuman Mishra",
"Dhruvesh Patel",
"Aparna Vijayakumar",
"Xiang Lorraine Li",
"Pavan Kapanipathi",
"Kartik Talamadupula"
] | Natural Language Inference (NLI) has garnered significant attention in recent years; however, the promise of applying NLI breakthroughs to other downstream NLP tasks has remained unfulfilled. In this work, we use the multiple-choice reading comprehension (MCRC) and checking factual correctness of textual summarization ... | 2021.naacl-main.104 | 10.18653/v1/2021.naacl-main.104 | null | 2009.09099 | title_judge |
2021.naacl-main.105 | Structure-Grounded Pretraining for Text-to-SQL | https://aclanthology.org/2021.naacl-main.105/ | [
"Xiang Deng",
"Ahmed Hassan Awadallah",
"Christopher Meek",
"Oleksandr Polozov",
"Huan Sun",
"Matthew Richardson"
] | Learning to capture text-table alignment is essential for tasks like text-to-SQL. A model needs to correctly recognize natural language references to columns and values and to ground them in the given database schema. In this paper, we present a novel weakly supervised Structure-Grounded pretraining framework (STRUG) f... | 2021.naacl-main.105 | 10.18653/v1/2021.naacl-main.105 | null | 2010.12773 | title_snapshot |
2021.naacl-main.106 | Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System | https://aclanthology.org/2021.naacl-main.106/ | [
"Congying Xia",
"Wenpeng Yin",
"Yihao Feng",
"Philip Yu"
] | Text classification is usually studied by labeling natural language texts with relevant categories from a predefined set. In the real world, new classes might keep challenging the existing system with limited labeled data. The system should be intelligent enough to recognize upcoming new classes with a few examples. In... | 2021.naacl-main.106 | 10.18653/v1/2021.naacl-main.106 | null | 2104.11882 | title_snapshot |
2021.naacl-main.107 | Temporal Reasoning on Implicit Events from Distant Supervision | https://aclanthology.org/2021.naacl-main.107/ | [
"Ben Zhou",
"Kyle Richardson",
"Qiang Ning",
"Tushar Khot",
"Ashish Sabharwal",
"Dan Roth"
] | We propose TRACIE, a novel temporal reasoning dataset that evaluates the degree to which systems understand implicit events—events that are not mentioned explicitly in natural language text but can be inferred from it. This introduces a new challenge in temporal reasoning research, where prior work has focused on expli... | 2021.naacl-main.107 | 10.18653/v1/2021.naacl-main.107 | null | 2010.12753 | title_snapshot |
2021.naacl-main.108 | Disentangling Semantics and Syntax in Sentence Embeddings with Pre-trained Language Models | https://aclanthology.org/2021.naacl-main.108/ | [
"James Y. Huang",
"Kuan-Hao Huang",
"Kai-Wei Chang"
] | Pre-trained language models have achieved huge success on a wide range of NLP tasks. However, contextual representations from pre-trained models contain entangled semantic and syntactic information, and therefore cannot be directly used to derive useful semantic sentence embeddings for some tasks. Paraphrase pairs offe... | 2021.naacl-main.108 | 10.18653/v1/2021.naacl-main.108 | null | 2104.05115 | title_snapshot |
2021.naacl-main.109 | Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs | https://aclanthology.org/2021.naacl-main.109/ | [
"Jiaao Chen",
"Diyi Yang"
] | Abstractive conversation summarization has received much attention recently. However, these generated summaries often suffer from insufficient, redundant, or incorrect content, largely due to the unstructured and complex characteristics of human-human interactions. To this end, we propose to explicitly model the rich s... | 2021.naacl-main.109 | 10.18653/v1/2021.naacl-main.109 | null | 2104.08400 | title_snapshot |
2021.naacl-main.110 | A New Approach to Overgenerating and Scoring Abstractive Summaries | https://aclanthology.org/2021.naacl-main.110/ | [
"Kaiqiang Song",
"Bingqing Wang",
"Zhe Feng",
"Fei Liu"
] | We propose a new approach to generate multiple variants of the target summary with diverse content and varying lengths, then score and select admissible ones according to users’ needs. Abstractive summarizers trained on single reference summaries may struggle to produce outputs that achieve multiple desirable propertie... | 2021.naacl-main.110 | 10.18653/v1/2021.naacl-main.110 | null | 2104.01726 | title_snapshot |
2021.naacl-main.111 | D2S: Document-to-Slide Generation Via Query-Based Text Summarization | https://aclanthology.org/2021.naacl-main.111/ | [
"Edward Sun",
"Yufang Hou",
"Dakuo Wang",
"Yunfeng Zhang",
"Nancy X. R. Wang"
] | Presentations are critical for communication in all areas of our lives, yet the creation of slide decks is often tedious and time-consuming. There has been limited research aiming to automate the document-to-slides generation process and all face a critical challenge: no publicly available dataset for training and benc... | 2021.naacl-main.111 | 10.18653/v1/2021.naacl-main.111 | null | 2105.03664 | title_snapshot |
2021.naacl-main.112 | Efficient Attentions for Long Document Summarization | https://aclanthology.org/2021.naacl-main.112/ | [
"Luyang Huang",
"Shuyang Cao",
"Nikolaus Parulian",
"Heng Ji",
"Lu Wang"
] | The quadratic computational and memory complexities of large Transformers have limited their scalability for long document summarization. In this paper, we propose Hepos, a novel efficient encoder-decoder attention with head-wise positional strides to effectively pinpoint salient information from the source. We further... | 2021.naacl-main.112 | 10.18653/v1/2021.naacl-main.112 | null | 2104.02112 | title_snapshot |
2021.naacl-main.113 | RefSum: Refactoring Neural Summarization | https://aclanthology.org/2021.naacl-main.113/ | [
"Yixin Liu",
"Zi-Yi Dou",
"Pengfei Liu"
] | Although some recent works show potential complementarity among different state-of-the-art systems, few works try to investigate this problem in text summarization. Researchers in other areas commonly refer to the techniques of reranking or stacking to approach this problem. In this work, we highlight several limitatio... | 2021.naacl-main.113 | 10.18653/v1/2021.naacl-main.113 | null | 2104.07210 | title_snapshot |
2021.naacl-main.114 | Annotating and Modeling Fine-grained Factuality in Summarization | https://aclanthology.org/2021.naacl-main.114/ | [
"Tanya Goyal",
"Greg Durrett"
] | Recent pre-trained abstractive summarization systems have started to achieve credible performance, but a major barrier to their use in practice is their propensity to output summaries that are not faithful to the input and that contain factual errors. While a number of annotated datasets and statistical models for asse... | 2021.naacl-main.114 | 10.18653/v1/2021.naacl-main.114 | null | 2104.04302 | title_snapshot |
2021.naacl-main.115 | Larger-Context Tagging: When and Why Does It Work? | https://aclanthology.org/2021.naacl-main.115/ | [
"Jinlan Fu",
"Liangjing Feng",
"Qi Zhang",
"Xuanjing Huang",
"Pengfei Liu"
] | The development of neural networks and pretraining techniques has spawned many sentence-level tagging systems that achieved superior performance on typical benchmarks. However, a relatively less discussed topic is what if more context information is introduced into current top-scoring tagging systems. Although several ... | 2021.naacl-main.115 | 10.18653/v1/2021.naacl-main.115 | null | 2104.04434 | title_snapshot |
2021.naacl-main.116 | Neural Sequence Segmentation as Determining the Leftmost Segments | https://aclanthology.org/2021.naacl-main.116/ | [
"Yangming Li",
"Lemao Liu",
"Kaisheng Yao"
] | Prior methods to text segmentation are mostly at token level. Despite the adequacy, this nature limits their full potential to capture the long-term dependencies among segments. In this work, we propose a novel framework that incrementally segments natural language sentences at segment level. For every step in segmenta... | 2021.naacl-main.116 | 10.18653/v1/2021.naacl-main.116 | null | 2104.07217 | title_snapshot |
2021.naacl-main.117 | PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols | https://aclanthology.org/2021.naacl-main.117/ | [
"Songlin Yang",
"Yanpeng Zhao",
"Kewei Tu"
] | Probabilistic context-free grammars (PCFGs) with neural parameterization have been shown to be effective in unsupervised phrase-structure grammar induction. However, due to the cubic computational complexity of PCFG representation and parsing, previous approaches cannot scale up to a relatively large number of (nonterm... | 2021.naacl-main.117 | 10.18653/v1/2021.naacl-main.117 | null | 2104.13727 | title_snapshot |
2021.naacl-main.118 | GEMNET: Effective Gated Gazetteer Representations for Recognizing Complex Entities in Low-context Input | https://aclanthology.org/2021.naacl-main.118/ | [
"Tao Meng",
"Anjie Fang",
"Oleg Rokhlenko",
"Shervin Malmasi"
] | Named Entity Recognition (NER) remains difficult in real-world settings; current challenges include short texts (low context), emerging entities, and complex entities (e.g. movie names). Gazetteer features can help, but results have been mixed due to challenges with adding extra features, and a lack of realistic evalua... | 2021.naacl-main.118 | 10.18653/v1/2021.naacl-main.118 | null | null | null |
2021.naacl-main.119 | Video-aided Unsupervised Grammar Induction | https://aclanthology.org/2021.naacl-main.119/ | [
"Songyang Zhang",
"Linfeng Song",
"Lifeng Jin",
"Kun Xu",
"Dong Yu",
"Jiebo Luo"
] | We investigate video-aided grammar induction, which learns a constituency parser from both unlabeled text and its corresponding video. Existing methods of multi-modal grammar induction focus on grammar induction from text-image pairs, with promising results showing that the information from static images is useful in i... | 2021.naacl-main.119 | 10.18653/v1/2021.naacl-main.119 | Best Long Paper | 2104.04369 | title_snapshot |
2021.naacl-main.120 | Generating Negative Samples by Manipulating Golden Responses for Unsupervised Learning of a Response Evaluation Model | https://aclanthology.org/2021.naacl-main.120/ | [
"ChaeHun Park",
"Eugene Jang",
"Wonsuk Yang",
"Jong Park"
] | Evaluating the quality of responses generated by open-domain conversation systems is a challenging task. This is partly because there can be multiple appropriate responses to a given dialogue history. Reference-based metrics that rely on comparisons to a set of known correct responses often fail to account for this var... | 2021.naacl-main.120 | 10.18653/v1/2021.naacl-main.120 | null | null | null |
2021.naacl-main.121 | How Robust are Fact Checking Systems on Colloquial Claims? | https://aclanthology.org/2021.naacl-main.121/ | [
"Byeongchang Kim",
"Hyunwoo Kim",
"Seokhee Hong",
"Gunhee Kim"
] | Knowledge is now starting to power neural dialogue agents. At the same time, the risk of misinformation and disinformation from dialogue agents also rises. Verifying the veracity of information from formal sources are widely studied in computational fact checking. In this work, we ask: How robust are fact checking syst... | 2021.naacl-main.121 | 10.18653/v1/2021.naacl-main.121 | null | null | null |
2021.naacl-main.122 | Fine-grained Post-training for Improving Retrieval-based Dialogue Systems | https://aclanthology.org/2021.naacl-main.122/ | [
"Janghoon Han",
"Taesuk Hong",
"Byoungjae Kim",
"Youngjoong Ko",
"Jungyun Seo"
] | Retrieval-based dialogue systems display an outstanding performance when pre-trained language models are used, which includes bidirectional encoder representations from transformers (BERT). During the multi-turn response selection, BERT focuses on training the relationship between the context with multiple utterances a... | 2021.naacl-main.122 | 10.18653/v1/2021.naacl-main.122 | null | null | null |
2021.naacl-main.123 | Put Chatbot into Its Interlocutor’s Shoes: New Framework to Learn Chatbot Responding with Intention | https://aclanthology.org/2021.naacl-main.123/ | [
"Hsuan Su",
"Jiun-Hao Jhan",
"Fan-yun Sun",
"Saurav Sahay",
"Hung-yi Lee"
] | Most chatbot literature that focuses on improving the fluency and coherence of a chatbot, is dedicated to making chatbots more human-like. However, very little work delves into what really separates humans from chatbots – humans intrinsically understand the effect their responses have on the interlocutor and often resp... | 2021.naacl-main.123 | 10.18653/v1/2021.naacl-main.123 | null | 2103.16429 | title_snapshot |
2021.naacl-main.124 | Adding Chit-Chat to Enhance Task-Oriented Dialogues | https://aclanthology.org/2021.naacl-main.124/ | [
"Kai Sun",
"Seungwhan Moon",
"Paul Crook",
"Stephen Roller",
"Becka Silvert",
"Bing Liu",
"Zhiguang Wang",
"Honglei Liu",
"Eunjoon Cho",
"Claire Cardie"
] | Existing dialogue corpora and models are typically designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e.g., booking hotels), open-domain chatbots aim at making socially engaging conversations. In this work, we propose to integrate both types of systems by Adding Chit-C... | 2021.naacl-main.124 | 10.18653/v1/2021.naacl-main.124 | null | 2010.12757 | title_snapshot |
2021.naacl-main.125 | Incorporating Syntax and Semantics in Coreference Resolution with Heterogeneous Graph Attention Network | https://aclanthology.org/2021.naacl-main.125/ | [
"Fan Jiang",
"Trevor Cohn"
] | External syntactic and semantic information has been largely ignored by existing neural coreference resolution models. In this paper, we present a heterogeneous graph-based model to incorporate syntactic and semantic structures of sentences. The proposed graph contains a syntactic sub-graph where tokens are connected b... | 2021.naacl-main.125 | 10.18653/v1/2021.naacl-main.125 | null | null | null |
2021.naacl-main.126 | Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition | https://aclanthology.org/2021.naacl-main.126/ | [
"Yingxue Zhang",
"Fandong Meng",
"Peng Li",
"Ping Jian",
"Jie Zhou"
] | Implicit discourse relation recognition (IDRR) aims to identify logical relations between two adjacent sentences in the discourse. Existing models fail to fully utilize the contextual information which plays an important role in interpreting each local sentence. In this paper, we thus propose a novel graph-based Contex... | 2021.naacl-main.126 | 10.18653/v1/2021.naacl-main.126 | null | null | null |
2021.naacl-main.127 | Improving Neural RST Parsing Model with Silver Agreement Subtrees | https://aclanthology.org/2021.naacl-main.127/ | [
"Naoki Kobayashi",
"Tsutomu Hirao",
"Hidetaka Kamigaito",
"Manabu Okumura",
"Masaaki Nagata"
] | Most of the previous Rhetorical Structure Theory (RST) parsing methods are based on supervised learning such as neural networks, that require an annotated corpus of sufficient size and quality. However, the RST Discourse Treebank (RST-DT), the benchmark corpus for RST parsing in English, is small due to the costly anno... | 2021.naacl-main.127 | 10.18653/v1/2021.naacl-main.127 | null | null | null |
2021.naacl-main.128 | RST Parsing from Scratch | https://aclanthology.org/2021.naacl-main.128/ | [
"Thanh-Tung Nguyen",
"Xuan-Phi Nguyen",
"Shafiq Joty",
"Xiaoli Li"
] | We introduce a novel top-down end-to-end formulation of document level discourse parsing in the Rhetorical Structure Theory (RST) framework. In this formulation, we consider discourse parsing as a sequence of splitting decisions at token boundaries and use a seq2seq network to model the splitting decisions. Our framewo... | 2021.naacl-main.128 | 10.18653/v1/2021.naacl-main.128 | null | 2105.10861 | title_snapshot |
2021.naacl-main.129 | Did they answer? Subjective acts and intents in conversational discourse | https://aclanthology.org/2021.naacl-main.129/ | [
"Elisa Ferracane",
"Greg Durrett",
"Junyi Jessy Li",
"Katrin Erk"
] | Discourse signals are often implicit, leaving it up to the interpreter to draw the required inferences. At the same time, discourse is embedded in a social context, meaning that interpreters apply their own assumptions and beliefs when resolving these inferences, leading to multiple, valid interpretations. However, cur... | 2021.naacl-main.129 | 10.18653/v1/2021.naacl-main.129 | null | 2104.04470 | title_snapshot |
2021.naacl-main.130 | Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance | https://aclanthology.org/2021.naacl-main.130/ | [
"Sopan Khosla",
"James Fiacco",
"Carolyn Rosé"
] | Recent work on entity coreference resolution (CR) follows current trends in Deep Learning applied to embeddings and relatively simple task-related features. SOTA models do not make use of hierarchical representations of discourse structure. In this work, we leverage automatically constructed discourse parse trees withi... | 2021.naacl-main.130 | 10.18653/v1/2021.naacl-main.130 | null | 2104.10215 | title_snapshot |
2021.naacl-main.131 | Bridging Resolution: Making Sense of the State of the Art | https://aclanthology.org/2021.naacl-main.131/ | [
"Hideo Kobayashi",
"Vincent Ng"
] | While Yu and Poesio (2020) have recently demonstrated the superiority of their neural multi-task learning (MTL) model to rule-based approaches for bridging anaphora resolution, there is little understanding of (1) how it is better than the rule-based approaches (e.g., are the two approaches making similar or complement... | 2021.naacl-main.131 | 10.18653/v1/2021.naacl-main.131 | null | null | null |
2021.naacl-main.132 | Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle | https://aclanthology.org/2021.naacl-main.132/ | [
"Yikang Shen",
"Shawn Tan",
"Alessandro Sordoni",
"Siva Reddy",
"Aaron Courville"
] | Syntax is fundamental to our thinking about language. Failing to capture the structure of input language could lead to generalization problems and over-parametrization. In the present work, we propose a new syntax-aware language model: Syntactic Ordered Memory (SOM). The model explicitly models the structure with an in... | 2021.naacl-main.132 | 10.18653/v1/2021.naacl-main.132 | null | 2011.07960 | title_snapshot |
2021.naacl-main.133 | Revisiting the Weaknesses of Reinforcement Learning for Neural Machine Translation | https://aclanthology.org/2021.naacl-main.133/ | [
"Samuel Kiegeland",
"Julia Kreutzer"
] | Policy gradient algorithms have found wide adoption in NLP, but have recently become subject to criticism, doubting their suitability for NMT. Choshen et al. (2020) identify multiple weaknesses and suspect that their success is determined by the shape of output distributions rather than the reward. In this paper, we re... | 2021.naacl-main.133 | 10.18653/v1/2021.naacl-main.133 | null | 2106.08942 | title_snapshot |
2021.naacl-main.134 | Learning to Organize a Bag of Words into Sentences with Neural Networks: An Empirical Study | https://aclanthology.org/2021.naacl-main.134/ | [
"Chongyang Tao",
"Shen Gao",
"Juntao Li",
"Yansong Feng",
"Dongyan Zhao",
"Rui Yan"
] | Sequential information, a.k.a., orders, is assumed to be essential for processing a sequence with recurrent neural network or convolutional neural network based encoders. However, is it possible to encode natural languages without orders? Given a bag of words from a disordered sentence, humans may still be able to unde... | 2021.naacl-main.134 | 10.18653/v1/2021.naacl-main.134 | null | null | null |
2021.naacl-main.135 | Mask Attention Networks: Rethinking and Strengthen Transformer | https://aclanthology.org/2021.naacl-main.135/ | [
"Zhihao Fan",
"Yeyun Gong",
"Dayiheng Liu",
"Zhongyu Wei",
"Siyuan Wang",
"Jian Jiao",
"Nan Duan",
"Ruofei Zhang",
"Xuanjing Huang"
] | Transformer is an attention-based neural network, which consists of two sublayers, namely, Self-Attention Network (SAN) and Feed-Forward Network (FFN). Existing research explores to enhance the two sublayers separately to improve the capability of Transformer for text representation. In this paper, we present a novel u... | 2021.naacl-main.135 | 10.18653/v1/2021.naacl-main.135 | null | 2103.13597 | title_snapshot |
2021.naacl-main.136 | ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding | https://aclanthology.org/2021.naacl-main.136/ | [
"Dongling Xiao",
"Yu-Kun Li",
"Han Zhang",
"Yu Sun",
"Hao Tian",
"Hua Wu",
"Haifeng Wang"
] | Coarse-grained linguistic information, such as named entities or phrases, facilitates adequately representation learning in pre-training. Previous works mainly focus on extending the objective of BERT’s Masked Language Modeling (MLM) from masking individual tokens to contiguous sequences of n tokens. We argue that such... | 2021.naacl-main.136 | 10.18653/v1/2021.naacl-main.136 | null | 2010.12148 | title_snapshot |
2021.naacl-main.137 | Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models | https://aclanthology.org/2021.naacl-main.137/ | [
"Yuxuan Lai",
"Yijia Liu",
"Yansong Feng",
"Songfang Huang",
"Dongyan Zhao"
] | Chinese pre-trained language models usually process text as a sequence of characters, while ignoring more coarse granularity, e.g., words. In this work, we propose a novel pre-training paradigm for Chinese — Lattice-BERT, which explicitly incorporates word representations along with characters, thus can model a sentenc... | 2021.naacl-main.137 | 10.18653/v1/2021.naacl-main.137 | null | 2104.07204 | title_snapshot |
2021.naacl-main.138 | Modeling Event Plausibility with Consistent Conceptual Abstraction | https://aclanthology.org/2021.naacl-main.138/ | [
"Ian Porada",
"Kaheer Suleman",
"Adam Trischler",
"Jackie Chi Kit Cheung"
] | Understanding natural language requires common sense, one aspect of which is the ability to discern the plausibility of events. While distributional models—most recently pre-trained, Transformer language models—have demonstrated improvements in modeling event plausibility, their performance still falls short of humans’... | 2021.naacl-main.138 | 10.18653/v1/2021.naacl-main.138 | null | 2104.10247 | title_snapshot |
2021.naacl-main.139 | UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus | https://aclanthology.org/2021.naacl-main.139/ | [
"George Michalopoulos",
"Yuanxin Wang",
"Hussam Kaka",
"Helen Chen",
"Alexander Wong"
] | Contextual word embedding models, such as BioBERT and Bio_ClinicalBERT, have achieved state-of-the-art results in biomedical natural language processing tasks by focusing their pre-training process on domain-specific corpora. However, such models do not take into consideration structured expert domain knowledge from a ... | 2021.naacl-main.139 | 10.18653/v1/2021.naacl-main.139 | null | 2010.10391 | title_snapshot |
2021.naacl-main.140 | Field Embedding: A Unified Grain-Based Framework for Word Representation | https://aclanthology.org/2021.naacl-main.140/ | [
"Junjie Luo",
"Xi Chen",
"Jichao Sun",
"Yuejia Xiang",
"Ningyu Zhang",
"Xiang Wan"
] | Word representations empowered with additional linguistic information have been widely studied and proved to outperform traditional embeddings. Current methods mainly focus on learning embeddings for words while embeddings of linguistic information (referred to as grain embeddings) are discarded after the learning. Thi... | 2021.naacl-main.140 | 10.18653/v1/2021.naacl-main.140 | null | null | null |
2021.naacl-main.141 | MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories | https://aclanthology.org/2021.naacl-main.141/ | [
"Minjin Choi",
"Sunkyung Lee",
"Eunseong Choi",
"Heesoo Park",
"Junhyuk Lee",
"Dongwon Lee",
"Jongwuk Lee"
] | Automated metaphor detection is a challenging task to identify the metaphorical expression of words in a sentence. To tackle this problem, we adopt pre-trained contextualized models, e.g., BERT and RoBERTa. To this end, we propose a novel metaphor detection model, namely metaphor-aware late interaction over BERT (MelBE... | 2021.naacl-main.141 | 10.18653/v1/2021.naacl-main.141 | null | 2104.13615 | title_snapshot |
2021.naacl-main.142 | Non-Parametric Few-Shot Learning for Word Sense Disambiguation | https://aclanthology.org/2021.naacl-main.142/ | [
"Howard Chen",
"Mengzhou Xia",
"Danqi Chen"
] | Word sense disambiguation (WSD) is a long-standing problem in natural language processing. One significant challenge in supervised all-words WSD is to classify among senses for a majority of words that lie in the long-tail distribution. For instance, 84% of the annotated words have less than 10 examples in the SemCor t... | 2021.naacl-main.142 | 10.18653/v1/2021.naacl-main.142 | null | 2104.12677 | title_snapshot |
2021.naacl-main.143 | Why Do Document-Level Polarity Classifiers Fail? | https://aclanthology.org/2021.naacl-main.143/ | [
"Karen Martins",
"Pedro O.S Vaz-de-Melo",
"Rodrygo Santos"
] | Machine learning solutions are often criticized for the lack of explanation of their successes and failures. Understanding which instances are misclassified and why is essential to improve the learning process. This work helps to fill this gap by proposing a methodology to characterize, quantify and measure the impact ... | 2021.naacl-main.143 | 10.18653/v1/2021.naacl-main.143 | null | null | null |
2021.naacl-main.144 | A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents | https://aclanthology.org/2021.naacl-main.144/ | [
"Qingrong Xia",
"Bo Zhang",
"Rui Wang",
"Zhenghua Li",
"Yue Zhang",
"Fei Huang",
"Luo Si",
"Min Zhang"
] | Fine-grained opinion mining (OM) has achieved increasing attraction in the natural language processing (NLP) community, which aims to find the opinion structures of “Who expressed what opinions towards what” in one sentence. In this work, motivated by its span-based representations of opinion expressions and roles, we ... | 2021.naacl-main.144 | 10.18653/v1/2021.naacl-main.144 | null | null | null |
2021.naacl-main.145 | Target-specified Sequence Labeling with Multi-head Self-attention for Target-oriented Opinion Words Extraction | https://aclanthology.org/2021.naacl-main.145/ | [
"Yuhao Feng",
"Yanghui Rao",
"Yuyao Tang",
"Ninghua Wang",
"He Liu"
] | Opinion target extraction and opinion term extraction are two fundamental tasks in Aspect Based Sentiment Analysis (ABSA). Many recent works on ABSA focus on Target-oriented Opinion Words (or Terms) Extraction (TOWE), which aims at extracting the corresponding opinion words for a given opinion target. TOWE can be furth... | 2021.naacl-main.145 | 10.18653/v1/2021.naacl-main.145 | null | null | null |
2021.naacl-main.146 | Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa | https://aclanthology.org/2021.naacl-main.146/ | [
"Junqi Dai",
"Hang Yan",
"Tianxiang Sun",
"Pengfei Liu",
"Xipeng Qiu"
] | Aspect-based Sentiment Analysis (ABSA), aiming at predicting the polarities for aspects, is a fine-grained task in the field of sentiment analysis. Previous work showed syntactic information, e.g. dependency trees, can effectively improve the ABSA performance. Recently, pre-trained models (PTMs) also have shown their e... | 2021.naacl-main.146 | 10.18653/v1/2021.naacl-main.146 | null | 2104.04986 | title_snapshot |
2021.naacl-main.147 | Domain Divergences: A Survey and Empirical Analysis | https://aclanthology.org/2021.naacl-main.147/ | [
"Abhinav Ramesh Kashyap",
"Devamanyu Hazarika",
"Min-Yen Kan",
"Roger Zimmermann"
] | Domain divergence plays a significant role in estimating the performance of a model in new domains. While there is a significant literature on divergence measures, researchers find it hard to choose an appropriate divergence for a given NLP application. We address this shortcoming by both surveying the literature and t... | 2021.naacl-main.147 | 10.18653/v1/2021.naacl-main.147 | null | 2010.12198 | title_snapshot |
2021.naacl-main.148 | Target-Aware Data Augmentation for Stance Detection | https://aclanthology.org/2021.naacl-main.148/ | [
"Yingjie Li",
"Cornelia Caragea"
] | The goal of stance detection is to identify whether the author of a text is in favor of, neutral or against a specific target. Despite substantial progress on this task, one of the remaining challenges is the scarcity of annotations. Data augmentation is commonly used to address annotation scarcity by generating more t... | 2021.naacl-main.148 | 10.18653/v1/2021.naacl-main.148 | null | null | null |
2021.naacl-main.149 | End-to-end ASR to jointly predict transcriptions and linguistic annotations | https://aclanthology.org/2021.naacl-main.149/ | [
"Motoi Omachi",
"Yuya Fujita",
"Shinji Watanabe",
"Matthew Wiesner"
] | We propose a Transformer-based sequence-to-sequence model for automatic speech recognition (ASR) capable of simultaneously transcribing and annotating audio with linguistic information such as phonemic transcripts or part-of-speech (POS) tags. Since linguistic information is important in natural language processing (NL... | 2021.naacl-main.149 | 10.18653/v1/2021.naacl-main.149 | null | null | null |
2021.naacl-main.150 | Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation | https://aclanthology.org/2021.naacl-main.150/ | [
"Hirofumi Inaguma",
"Tatsuya Kawahara",
"Shinji Watanabe"
] | A conventional approach to improving the performance of end-to-end speech translation (E2E-ST) models is to leverage the source transcription via pre-training and joint training with automatic speech recognition (ASR) and neural machine translation (NMT) tasks. However, since the input modalities are different, it is d... | 2021.naacl-main.150 | 10.18653/v1/2021.naacl-main.150 | null | 2104.06457 | title_snapshot |
2021.naacl-main.151 | Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks | https://aclanthology.org/2021.naacl-main.151/ | [
"Siddharth Dalmia",
"Brian Yan",
"Vikas Raunak",
"Florian Metze",
"Shinji Watanabe"
] | End-to-end approaches for sequence tasks are becoming increasingly popular. Yet for complex sequence tasks, like speech translation, systems that cascade several models trained on sub-tasks have shown to be superior, suggesting that the compositionality of cascaded systems simplifies learning and enables sophisticated ... | 2021.naacl-main.151 | 10.18653/v1/2021.naacl-main.151 | null | 2105.00573 | title_snapshot |
2021.naacl-main.152 | SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding | https://aclanthology.org/2021.naacl-main.152/ | [
"Yu-An Chung",
"Chenguang Zhu",
"Michael Zeng"
] | Spoken language understanding (SLU) requires a model to analyze input acoustic signal to understand its linguistic content and make predictions. To boost the models’ performance, various pre-training methods have been proposed to learn rich representations from large-scale unannotated speech and text. However, the inhe... | 2021.naacl-main.152 | 10.18653/v1/2021.naacl-main.152 | null | 2010.02295 | title_snapshot |
2021.naacl-main.153 | Worldly Wise (WoW) - Cross-Lingual Knowledge Fusion for Fact-based Visual Spoken-Question Answering | https://aclanthology.org/2021.naacl-main.153/ | [
"Kiran Ramnath",
"Leda Sari",
"Mark Hasegawa-Johnson",
"Chang Yoo"
] | Although Question-Answering has long been of research interest, its accessibility to users through a speech interface and its support to multiple languages have not been addressed in prior studies. Towards these ends, we present a new task and a synthetically-generated dataset to do Fact-based Visual Spoken-Question An... | 2021.naacl-main.153 | 10.18653/v1/2021.naacl-main.153 | null | null | null |
2021.naacl-main.154 | Align-Refine: Non-Autoregressive Speech Recognition via Iterative Realignment | https://aclanthology.org/2021.naacl-main.154/ | [
"Ethan A. Chi",
"Julian Salazar",
"Katrin Kirchhoff"
] | Non-autoregressive encoder-decoder models greatly improve decoding speed over autoregressive models, at the expense of generation quality. To mitigate this, iterative decoding models repeatedly infill or refine the proposal of a non-autoregressive model. However, editing at the level of output sequences limits model fl... | 2021.naacl-main.154 | 10.18653/v1/2021.naacl-main.154 | null | 2010.14233 | title_snapshot |
2021.naacl-main.155 | Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis | https://aclanthology.org/2021.naacl-main.155/ | [
"Xiao Liu",
"Da Yin",
"Yansong Feng",
"Yuting Wu",
"Dongyan Zhao"
] | Causal inference is the process of capturing cause-effect relationship among variables. Most existing works focus on dealing with structured data, while mining causal relationship among factors from unstructured data, like text, has been less examined, but is of great importance, especially in the legal domain. In this... | 2021.naacl-main.155 | 10.18653/v1/2021.naacl-main.155 | null | 2104.09420 | title_snapshot |
2021.naacl-main.156 | Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network | https://aclanthology.org/2021.naacl-main.156/ | [
"Haoran Wu",
"Wei Chen",
"Shuang Xu",
"Bo Xu"
] | Providing a reliable explanation for clinical diagnosis based on the Electronic Medical Record (EMR) is fundamental to the application of Artificial Intelligence in the medical field. Current methods mostly treat the EMR as a text sequence and provide explanations based on a precise medical knowledge base, which is dis... | 2021.naacl-main.156 | 10.18653/v1/2021.naacl-main.156 | null | null | null |
2021.naacl-main.157 | Personalized Response Generation via Generative Split Memory Network | https://aclanthology.org/2021.naacl-main.157/ | [
"Yuwei Wu",
"Xuezhe Ma",
"Diyi Yang"
] | Despite the impressive successes of generation and dialogue systems, how to endow a text generation system with particular personality traits to deliver more personalized responses remains under-investigated. In this work, we look at how to generate personalized responses for questions on Reddit by utilizing personaliz... | 2021.naacl-main.157 | 10.18653/v1/2021.naacl-main.157 | null | null | null |
2021.naacl-main.158 | Towards Few-shot Fact-Checking via Perplexity | https://aclanthology.org/2021.naacl-main.158/ | [
"Nayeon Lee",
"Yejin Bang",
"Andrea Madotto",
"Pascale Fung"
] | Few-shot learning has drawn researchers’ attention to overcome the problem of data scarcity. Recently, large pre-trained language models have shown great performance in few-shot learning for various downstream tasks, such as question answering and machine translation. Nevertheless, little exploration has been made to a... | 2021.naacl-main.158 | 10.18653/v1/2021.naacl-main.158 | null | 2103.09535 | title_snapshot |
2021.naacl-main.159 | Active^2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation | https://aclanthology.org/2021.naacl-main.159/ | [
"Rishi Hazra",
"Parag Dutta",
"Shubham Gupta",
"Mohammed Abdul Qaathir",
"Ambedkar Dukkipati"
] | While deep learning is a powerful tool for natural language processing (NLP) problems, successful solutions to these problems rely heavily on large amounts of annotated samples. However, manually annotating data is expensive and time-consuming. Active Learning (AL) strategies reduce the need for huge volumes of labeled... | 2021.naacl-main.159 | 10.18653/v1/2021.naacl-main.159 | null | 1911.00234 | title_snapshot |
2021.naacl-main.160 | Generating An Optimal Interview Question Plan Using A Knowledge Graph And Integer Linear Programming | https://aclanthology.org/2021.naacl-main.160/ | [
"Soham Datta",
"Prabir Mallick",
"Sangameshwar Patil",
"Indrajit Bhattacharya",
"Girish Palshikar"
] | Given the diversity of the candidates and complexity of job requirements, and since interviewing is an inherently subjective process, it is an important task to ensure consistent, uniform, efficient and objective interviews that result in high quality recruitment. We propose an interview assistant system to automatical... | 2021.naacl-main.160 | 10.18653/v1/2021.naacl-main.160 | null | null | null |
2021.naacl-main.161 | Model Extraction and Adversarial Transferability, Your BERT is Vulnerable! | https://aclanthology.org/2021.naacl-main.161/ | [
"Xuanli He",
"Lingjuan Lyu",
"Qiongkai Xu",
"Lichao Sun"
] | Natural language processing (NLP) tasks, ranging from text classification to text generation, have been revolutionised by the pretrained language models, such as BERT. This allows corporations to easily build powerful APIs by encapsulating fine-tuned BERT models for downstream tasks. However, when a fine-tuned BERT mod... | 2021.naacl-main.161 | 10.18653/v1/2021.naacl-main.161 | null | 2103.10013 | title_snapshot |
2021.naacl-main.162 | A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models | https://aclanthology.org/2021.naacl-main.162/ | [
"Kaiyuan Liao",
"Yi Zhang",
"Xuancheng Ren",
"Qi Su",
"Xu Sun",
"Bin He"
] | Early exit mechanism aims to accelerate the inference speed of large-scale pre-trained language models. The essential idea is to exit early without passing through all the inference layers at the inference stage. To make accurate predictions for downstream tasks, the hierarchical linguistic information embedded in all ... | 2021.naacl-main.162 | 10.18653/v1/2021.naacl-main.162 | null | null | null |
2021.naacl-main.163 | Masked Conditional Random Fields for Sequence Labeling | https://aclanthology.org/2021.naacl-main.163/ | [
"Tianwen Wei",
"Jianwei Qi",
"Shenghuan He",
"Songtao Sun"
] | Conditional Random Field (CRF) based neural models are among the most performant methods for solving sequence labeling problems. Despite its great success, CRF has the shortcoming of occasionally generating illegal sequences of tags, e.g. sequences containing an “I-” tag immediately after an “O” tag, which is forbidden... | 2021.naacl-main.163 | 10.18653/v1/2021.naacl-main.163 | null | 2103.10682 | title_snapshot |
2021.naacl-main.164 | Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data | https://aclanthology.org/2021.naacl-main.164/ | [
"Chenghao Jia",
"Yongliang Shen",
"Yechun Tang",
"Lu Sun",
"Weiming Lu"
] | Prerequisite relations among concepts are crucial for educational applications, such as curriculum planning and intelligent tutoring. In this paper, we propose a novel concept prerequisite relation learning approach, named CPRL, which combines both concept representation learned from a heterogeneous graph and concept p... | 2021.naacl-main.164 | 10.18653/v1/2021.naacl-main.164 | null | null | null |
2021.naacl-main.165 | Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models | https://aclanthology.org/2021.naacl-main.165/ | [
"Wenkai Yang",
"Lei Li",
"Zhiyuan Zhang",
"Xuancheng Ren",
"Xu Sun",
"Bin He"
] | Recent studies have revealed a security threat to natural language processing (NLP) models, called the Backdoor Attack. Victim models can maintain competitive performance on clean samples while behaving abnormally on samples with a specific trigger word inserted. Previous backdoor attacking methods usually assume that ... | 2021.naacl-main.165 | 10.18653/v1/2021.naacl-main.165 | null | 2103.15543 | title_snapshot |
2021.naacl-main.166 | DA-Transformer: Distance-aware Transformer | https://aclanthology.org/2021.naacl-main.166/ | [
"Chuhan Wu",
"Fangzhao Wu",
"Yongfeng Huang"
] | Transformer has achieved great success in the NLP field by composing various advanced models like BERT and GPT. However, Transformer and its existing variants may not be optimal in capturing token distances because the position or distance embeddings used by these methods usually cannot keep the precise information of ... | 2021.naacl-main.166 | 10.18653/v1/2021.naacl-main.166 | null | 2010.06925 | title_snapshot |
2021.naacl-main.167 | ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction | https://aclanthology.org/2021.naacl-main.167/ | [
"Jiahao Bu",
"Lei Ren",
"Shuang Zheng",
"Yang Yang",
"Jingang Wang",
"Fuzheng Zhang",
"Wei Wu"
] | Sentiment analysis has attracted increasing attention in e-commerce. The sentiment polarities underlying user reviews are of great value for business intelligence. Aspect category sentiment analysis (ACSA) and review rating prediction (RP) are two essential tasks to detect the fine-to-coarse sentiment polarities. ACSA ... | 2021.naacl-main.167 | 10.18653/v1/2021.naacl-main.167 | null | 2103.06605 | title_snapshot |
2021.naacl-main.168 | Are NLP Models really able to Solve Simple Math Word Problems? | https://aclanthology.org/2021.naacl-main.168/ | [
"Arkil Patel",
"Satwik Bhattamishra",
"Navin Goyal"
] | The problem of designing NLP solvers for math word problems (MWP) has seen sustained research activity and steady gains in the test accuracy. Since existing solvers achieve high performance on the benchmark datasets for elementary level MWPs containing one-unknown arithmetic word problems, such problems are often consi... | 2021.naacl-main.168 | 10.18653/v1/2021.naacl-main.168 | null | 2103.07191 | title_snapshot |
2021.naacl-main.169 | WRIME: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations | https://aclanthology.org/2021.naacl-main.169/ | [
"Tomoyuki Kajiwara",
"Chenhui Chu",
"Noriko Takemura",
"Yuta Nakashima",
"Hajime Nagahara"
] | We annotate 17,000 SNS posts with both the writer’s subjective emotional intensity and the reader’s objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. We found that the reader c... | 2021.naacl-main.169 | 10.18653/v1/2021.naacl-main.169 | null | null | null |
2021.naacl-main.170 | KPQA: A Metric for Generative Question Answering Using Keyphrase Weights | https://aclanthology.org/2021.naacl-main.170/ | [
"Hwanhee Lee",
"Seunghyun Yoon",
"Franck Dernoncourt",
"Doo Soon Kim",
"Trung Bui",
"Joongbo Shin",
"Kyomin Jung"
] | In the automatic evaluation of generative question answering (GenQA) systems, it is difficult to assess the correctness of generated answers due to the free-form of the answer. Especially, widely used n-gram similarity metrics often fail to discriminate the incorrect answers since they equally consider all of the token... | 2021.naacl-main.170 | 10.18653/v1/2021.naacl-main.170 | null | 2005.00192 | title_snapshot |
2021.naacl-main.171 | StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer | https://aclanthology.org/2021.naacl-main.171/ | [
"Yiwei Lyu",
"Paul Pu Liang",
"Hai Pham",
"Eduard Hovy",
"Barnabás Póczos",
"Ruslan Salakhutdinov",
"Louis-Philippe Morency"
] | Text style transfer aims to controllably generate text with targeted stylistic changes while maintaining core meaning from the source sentence constant. Many of the existing style transfer benchmarks primarily focus on individual high-level semantic changes (e.g. positive to negative), which enable controllability at a... | 2021.naacl-main.171 | 10.18653/v1/2021.naacl-main.171 | null | 2104.05196 | title_snapshot |
2021.naacl-main.172 | Blow the Dog Whistle: A Chinese Dataset for Cant Understanding with Common Sense and World Knowledge | https://aclanthology.org/2021.naacl-main.172/ | [
"Canwen Xu",
"Wangchunshu Zhou",
"Tao Ge",
"Ke Xu",
"Julian McAuley",
"Furu Wei"
] | Cant is important for understanding advertising, comedies and dog-whistle politics. However, computational research on cant is hindered by a lack of available datasets. In this paper, we propose a large and diverse Chinese dataset for creating and understanding cant from a computational linguistics perspective. We form... | 2021.naacl-main.172 | 10.18653/v1/2021.naacl-main.172 | null | 2104.02704 | title_snapshot |
2021.naacl-main.173 | COVID-19 Named Entity Recognition for Vietnamese | https://aclanthology.org/2021.naacl-main.173/ | [
"Thinh Hung Truong",
"Mai Hoang Dao",
"Dat Quoc Nguyen"
] | The current COVID-19 pandemic has lead to the creation of many corpora that facilitate NLP research and downstream applications to help fight the pandemic. However, most of these corpora are exclusively for English. As the pandemic is a global problem, it is worth creating COVID-19 related datasets for languages other ... | 2021.naacl-main.173 | 10.18653/v1/2021.naacl-main.173 | null | 2104.03879 | title_snapshot |
2021.naacl-main.174 | Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames | https://aclanthology.org/2021.naacl-main.174/ | [
"Shima Khanehzar",
"Trevor Cohn",
"Gosia Mikolajczak",
"Andrew Turpin",
"Lea Frermann"
] | Understanding how news media frame political issues is important due to its impact on public attitudes, yet hard to automate. Computational approaches have largely focused on classifying the frame of a full news article while framing signals are often subtle and local. Furthermore, automatic news analysis is a sensitiv... | 2021.naacl-main.174 | 10.18653/v1/2021.naacl-main.174 | null | 2104.11030 | title_snapshot |
2021.naacl-main.175 | Automatic Classification of Neutralization Techniques in the Narrative of Climate Change Scepticism | https://aclanthology.org/2021.naacl-main.175/ | [
"Shraey Bhatia",
"Jey Han Lau",
"Timothy Baldwin"
] | Neutralisation techniques, e.g. denial of responsibility and denial of victim, are used in the narrative of climate change scepticism to justify lack of action or to promote an alternative view. We first draw on social science to introduce the problem to the community of nlp, present the granularity of the coding schem... | 2021.naacl-main.175 | 10.18653/v1/2021.naacl-main.175 | null | null | null |
2021.naacl-main.176 | Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning | https://aclanthology.org/2021.naacl-main.176/ | [
"Ramit Sawhney",
"Harshit Joshi",
"Rajiv Ratn Shah",
"Lucie Flek"
] | Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. Personally contextualizing the buildup of such ideation is critical for accurate identification of users at risk. In this work, we propose a framework jointly le... | 2021.naacl-main.176 | 10.18653/v1/2021.naacl-main.176 | null | null | null |
2021.naacl-main.177 | WikiTalkEdit: A Dataset for modeling Editors’ behaviors on Wikipedia | https://aclanthology.org/2021.naacl-main.177/ | [
"Kokil Jaidka",
"Andrea Ceolin",
"Iknoor Singh",
"Niyati Chhaya",
"Lyle Ungar"
] | This study introduces and analyzes WikiTalkEdit, a dataset of conversations and edit histories from Wikipedia, for research in online cooperation and conversation modeling. The dataset comprises dialog triplets from the Wikipedia Talk pages, and editing actions on the corresponding articles being discussed. We show how... | 2021.naacl-main.177 | 10.18653/v1/2021.naacl-main.177 | null | null | null |
2021.naacl-main.178 | The structure of online social networks modulates the rate of lexical change | https://aclanthology.org/2021.naacl-main.178/ | [
"Jian Zhu",
"David Jurgens"
] | New words are regularly introduced to communities, yet not all of these words persist in a community’s lexicon. Among the many factors contributing to lexical change, we focus on the understudied effect of social networks. We conduct a large-scale analysis of over 80k neologisms in 4420 online communities across a deca... | 2021.naacl-main.178 | 10.18653/v1/2021.naacl-main.178 | null | 2104.05010 | title_snapshot |
2021.naacl-main.179 | Modeling Framing in Immigration Discourse on Social Media | https://aclanthology.org/2021.naacl-main.179/ | [
"Julia Mendelsohn",
"Ceren Budak",
"David Jurgens"
] | The framing of political issues can influence policy and public opinion. Even though the public plays a key role in creating and spreading frames, little is known about how ordinary people on social media frame political issues. By creating a new dataset of immigration-related tweets labeled for multiple framing typolo... | 2021.naacl-main.179 | 10.18653/v1/2021.naacl-main.179 | null | 2104.06443 | title_snapshot |
2021.naacl-main.180 | Modeling the Severity of Complaints in Social Media | https://aclanthology.org/2021.naacl-main.180/ | [
"Mali Jin",
"Nikolaos Aletras"
] | The speech act of complaining is used by humans to communicate a negative mismatch between reality and expectations as a reaction to an unfavorable situation. Linguistic theory of pragmatics categorizes complaints into various severity levels based on the face-threat that the complainer is willing to undertake. This is... | 2021.naacl-main.180 | 10.18653/v1/2021.naacl-main.180 | null | 2103.12428 | title_snapshot |
2021.naacl-main.181 | What About the Precedent: An Information-Theoretic Analysis of Common Law | https://aclanthology.org/2021.naacl-main.181/ | [
"Josef Valvoda",
"Tiago Pimentel",
"Niklas Stoehr",
"Ryan Cotterell",
"Simone Teufel"
] | In common law, the outcome of a new case is determined mostly by precedent cases, rather than by existing statutes. However, how exactly does the precedent influence the outcome of a new case? Answering this question is crucial for guaranteeing fair and consistent judicial decision-making. We are the first to approach ... | 2021.naacl-main.181 | 10.18653/v1/2021.naacl-main.181 | null | 2104.12133 | title_snapshot |
2021.naacl-main.182 | Introducing CAD: the Contextual Abuse Dataset | https://aclanthology.org/2021.naacl-main.182/ | [
"Bertie Vidgen",
"Dong Nguyen",
"Helen Margetts",
"Patricia Rossini",
"Rebekah Tromble"
] | Online abuse can inflict harm on users and communities, making online spaces unsafe and toxic. Progress in automatically detecting and classifying abusive content is often held back by the lack of high quality and detailed datasets. We introduce a new dataset of primarily English Reddit entries which addresses several ... | 2021.naacl-main.182 | 10.18653/v1/2021.naacl-main.182 | null | null | null |
2021.naacl-main.183 | Lifelong Learning of Hate Speech Classification on Social Media | https://aclanthology.org/2021.naacl-main.183/ | [
"Jing Qian",
"Hong Wang",
"Mai ElSherief",
"Xifeng Yan"
] | Existing work on automated hate speech classification assumes that the dataset is fixed and the classes are pre-defined. However, the amount of data in social media increases every day, and the hot topics changes rapidly, requiring the classifiers to be able to continuously adapt to new data without forgetting the prev... | 2021.naacl-main.183 | 10.18653/v1/2021.naacl-main.183 | null | 2106.02821 | title_snapshot |
2021.naacl-main.184 | Learning to Recognize Dialect Features | https://aclanthology.org/2021.naacl-main.184/ | [
"Dorottya Demszky",
"Devyani Sharma",
"Jonathan Clark",
"Vinodkumar Prabhakaran",
"Jacob Eisenstein"
] | Building NLP systems that serve everyone requires accounting for dialect differences. But dialects are not monolithic entities: rather, distinctions between and within dialects are captured by the presence, absence, and frequency of dozens of dialect features in speech and text, such as the deletion of the copula in “H... | 2021.naacl-main.184 | 10.18653/v1/2021.naacl-main.184 | null | 2010.12707 | title_snapshot |
2021.naacl-main.185 | It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners | https://aclanthology.org/2021.naacl-main.185/ | [
"Timo Schick",
"Hinrich Schütze"
] | When scaled to hundreds of billions of parameters, pretrained language models such as GPT-3 (Brown et al., 2020) achieve remarkable few-shot performance. However, enormous amounts of compute are required for training and applying such big models, resulting in a large carbon footprint and making it difficult for researc... | 2021.naacl-main.185 | 10.18653/v1/2021.naacl-main.185 | Outstanding Long Paper | 2009.07118 | title_snapshot |
2021.naacl-main.186 | Static Embeddings as Efficient Knowledge Bases? | https://aclanthology.org/2021.naacl-main.186/ | [
"Philipp Dufter",
"Nora Kassner",
"Hinrich Schütze"
] | Recent research investigates factual knowledge stored in large pretrained language models (PLMs). Instead of structural knowledge base (KB) queries, masked sentences such as “Paris is the capital of [MASK]” are used as probes. The good performance on this analysis task has been interpreted as PLMs becoming potential re... | 2021.naacl-main.186 | 10.18653/v1/2021.naacl-main.186 | null | 2104.07094 | title_snapshot |
2021.naacl-main.187 | Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis | https://aclanthology.org/2021.naacl-main.187/ | [
"Xutan Peng",
"Guanyi Chen",
"Chenghua Lin",
"Mark Stevenson"
] | Knowledge Graph Embeddings (KGEs) have been intensively explored in recent years due to their promise for a wide range of applications. However, existing studies focus on improving the final model performance without acknowledging the computational cost of the proposed approaches, in terms of execution time and environ... | 2021.naacl-main.187 | 10.18653/v1/2021.naacl-main.187 | null | 2104.04676 | title_snapshot |
2021.naacl-main.188 | Rethinking Network Pruning – under the Pre-train and Fine-tune Paradigm | https://aclanthology.org/2021.naacl-main.188/ | [
"Dongkuan Xu",
"Ian En-Hsu Yen",
"Jinxi Zhao",
"Zhibin Xiao"
] | Transformer-based pre-trained language models have significantly improved the performance of various natural language processing (NLP) tasks in the recent years. While effective and prevalent, these models are usually prohibitively large for resource-limited deployment scenarios. A thread of research has thus been work... | 2021.naacl-main.188 | 10.18653/v1/2021.naacl-main.188 | null | 2104.08682 | title_snapshot |
2021.naacl-main.189 | Towards a Comprehensive Understanding and Accurate Evaluation of Societal Biases in Pre-Trained Transformers | https://aclanthology.org/2021.naacl-main.189/ | [
"Andrew Silva",
"Pradyumna Tambwekar",
"Matthew Gombolay"
] | The ease of access to pre-trained transformers has enabled developers to leverage large-scale language models to build exciting applications for their users. While such pre-trained models offer convenient starting points for researchers and developers, there is little consideration for the societal biases captured with... | 2021.naacl-main.189 | 10.18653/v1/2021.naacl-main.189 | null | null | null |
2021.naacl-main.190 | Detoxifying Language Models Risks Marginalizing Minority Voices | https://aclanthology.org/2021.naacl-main.190/ | [
"Albert Xu",
"Eshaan Pathak",
"Eric Wallace",
"Suchin Gururangan",
"Maarten Sap",
"Dan Klein"
] | Language models (LMs) must be both safe and equitable to be responsibly deployed in practice. With safety in mind, numerous detoxification techniques (e.g., Dathathri et al. 2020; Krause et al. 2020) have been proposed to mitigate toxic LM generations. In this work, we show that these detoxification techniques hurt equ... | 2021.naacl-main.190 | 10.18653/v1/2021.naacl-main.190 | null | 2104.06390 | title_snapshot |
2021.naacl-main.191 | HONEST: Measuring Hurtful Sentence Completion in Language Models | https://aclanthology.org/2021.naacl-main.191/ | [
"Debora Nozza",
"Federico Bianchi",
"Dirk Hovy"
] | Language models have revolutionized the field of NLP. However, language models capture and proliferate hurtful stereotypes, especially in text generation. Our results show that 4.3% of the time, language models complete a sentence with a hurtful word. These cases are not random, but follow language and gender-specific ... | 2021.naacl-main.191 | 10.18653/v1/2021.naacl-main.191 | null | null | null |
2021.naacl-main.192 | EaSe: A Diagnostic Tool for VQA based on Answer Diversity | https://aclanthology.org/2021.naacl-main.192/ | [
"Shailza Jolly",
"Sandro Pezzelle",
"Moin Nabi"
] | We propose EASE, a simple diagnostic tool for Visual Question Answering (VQA) which quantifies the difficulty of an image, question sample. EASE is based on the pattern of answers provided by multiple annotators to a given question. In particular, it considers two aspects of the answers: (i) their Entropy; (ii) their S... | 2021.naacl-main.192 | 10.18653/v1/2021.naacl-main.192 | null | null | null |
2021.naacl-main.193 | DeCEMBERT: Learning from Noisy Instructional Videos via Dense Captions and Entropy Minimization | https://aclanthology.org/2021.naacl-main.193/ | [
"Zineng Tang",
"Jie Lei",
"Mohit Bansal"
] | Leveraging large-scale unlabeled web videos such as instructional videos for pre-training followed by task-specific finetuning has become the de facto approach for many video-and-language tasks. However, these instructional videos are very noisy, the accompanying ASR narrations are often incomplete, and can be irreleva... | 2021.naacl-main.193 | 10.18653/v1/2021.naacl-main.193 | null | null | null |
2021.naacl-main.194 | Improving Generation and Evaluation of Visual Stories via Semantic Consistency | https://aclanthology.org/2021.naacl-main.194/ | [
"Adyasha Maharana",
"Darryl Hannan",
"Mohit Bansal"
] | Story visualization is an underexplored task that falls at the intersection of many important research directions in both computer vision and natural language processing. In this task, given a series of natural language captions which compose a story, an agent must generate a sequence of images that correspond to the c... | 2021.naacl-main.194 | 10.18653/v1/2021.naacl-main.194 | null | 2105.10026 | title_snapshot |
2021.naacl-main.195 | Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models | https://aclanthology.org/2021.naacl-main.195/ | [
"Po-Yao Huang",
"Mandela Patrick",
"Junjie Hu",
"Graham Neubig",
"Florian Metze",
"Alexander Hauptmann"
] | This paper studies zero-shot cross-lingual transfer of vision-language models. Specifically, we focus on multilingual text-to-video search and propose a Transformer-based model that learns contextual multilingual multimodal embeddings. Under a zero-shot setting, we empirically demonstrate that performance degrades sign... | 2021.naacl-main.195 | 10.18653/v1/2021.naacl-main.195 | null | 2103.08849 | title_snapshot |
2021.naacl-main.196 | Video Question Answering with Phrases via Semantic Roles | https://aclanthology.org/2021.naacl-main.196/ | [
"Arka Sadhu",
"Kan Chen",
"Ram Nevatia"
] | Video Question Answering (VidQA) evaluation metrics have been limited to a single-word answer or selecting a phrase from a fixed set of phrases. These metrics limit the VidQA models’ application scenario. In this work, we leverage semantic roles derived from video descriptions to mask out certain phrases, to introduce ... | 2021.naacl-main.196 | 10.18653/v1/2021.naacl-main.196 | null | 2104.03762 | title_snapshot |
2021.naacl-main.197 | From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding | https://aclanthology.org/2021.naacl-main.197/ | [
"Rob van der Goot",
"Ibrahim Sharaf",
"Aizhan Imankulova",
"Ahmet Üstün",
"Marija Stepanović",
"Alan Ramponi",
"Siti Oryza Khairunnisa",
"Mamoru Komachi",
"Barbara Plank"
] | The lack of publicly available evaluation data for low-resource languages limits progress in Spoken Language Understanding (SLU). As key tasks like intent classification and slot filling require abundant training data, it is desirable to reuse existing data in high-resource languages to develop models for low-resource ... | 2021.naacl-main.197 | 10.18653/v1/2021.naacl-main.197 | null | 2105.07316 | title_snapshot |
2021.naacl-main.198 | WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia | https://aclanthology.org/2021.naacl-main.198/ | [
"Alon Eirew",
"Arie Cattan",
"Ido Dagan"
] | Cross-document event coreference resolution is a foundational task for NLP applications involving multi-text processing. However, existing corpora for this task are scarce and relatively small, while annotating only modest-size clusters of documents belonging to the same topic. To complement these resources and enhance... | 2021.naacl-main.198 | 10.18653/v1/2021.naacl-main.198 | null | 2104.05022 | title_snapshot |
2021.naacl-main.199 | Challenging distributional models with a conceptual network of philosophical terms | https://aclanthology.org/2021.naacl-main.199/ | [
"Yvette Oortwijn",
"Jelke Bloem",
"Pia Sommerauer",
"Francois Meyer",
"Wei Zhou",
"Antske Fokkens"
] | Computational linguistic research on language change through distributional semantic (DS) models has inspired researchers from fields such as philosophy and literary studies, who use these methods for the exploration and comparison of comparatively small datasets traditionally analyzed by close reading. Research on met... | 2021.naacl-main.199 | 10.18653/v1/2021.naacl-main.199 | null | null | null |
2021.naacl-main.200 | KILT: a Benchmark for Knowledge Intensive Language Tasks | https://aclanthology.org/2021.naacl-main.200/ | [
"Fabio Petroni",
"Aleksandra Piktus",
"Angela Fan",
"Patrick Lewis",
"Majid Yazdani",
"Nicola De Cao",
"James Thorne",
"Yacine Jernite",
"Vladimir Karpukhin",
"Jean Maillard",
"Vassilis Plachouras",
"Tim Rocktäschel",
"Sebastian Riedel"
] | Challenging problems such as open-domain question answering, fact checking, slot filling and entity linking require access to large, external knowledge sources. While some models do well on individual tasks, developing general models is difficult as each task might require computationally expensive indexing of custom k... | 2021.naacl-main.200 | 10.18653/v1/2021.naacl-main.200 | null | 2009.02252 | title_snapshot |
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