ACL
Collection
Accepted papers for ACL (Annual Meeting of the Association for Computational Linguistics), one dataset per year. • 14 items • Updated
paper_id stringlengths 15 18 | title stringlengths 22 143 | paper_url stringlengths 41 44 | authors listlengths 1 16 | abstract large_stringlengths 439 1.79k | anthology_id stringlengths 15 18 | doi stringlengths 27 30 | award stringclasses 3
values | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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2021.acl-long.1 | Investigating label suggestions for opinion mining in German Covid-19 social media | https://aclanthology.org/2021.acl-long.1/ | [
"Tilman Beck",
"Ji-Ung Lee",
"Christina Viehmann",
"Marcus Maurer",
"Oliver Quiring",
"Iryna Gurevych"
] | This work investigates the use of interactively updated label suggestions to improve upon the efficiency of gathering annotations on the task of opinion mining in German Covid-19 social media data. We develop guidelines to conduct a controlled annotation study with social science students and find that suggestions from... | 2021.acl-long.1 | 10.18653/v1/2021.acl-long.1 | null | 2105.12980 | title_snapshot |
2021.acl-long.2 | How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements | https://aclanthology.org/2021.acl-long.2/ | [
"Chen Shani",
"Nadav Borenstein",
"Dafna Shahaf"
] | Humor is an important social phenomenon, serving complex social and psychological functions. However, despite being studied for millennia humor is computationally not well understood, often considered an AI-complete problem. In this work, we introduce a novel setting in humor mining: automatically detecting funny and u... | 2021.acl-long.2 | 10.18653/v1/2021.acl-long.2 | null | 2106.03048 | title_snapshot |
2021.acl-long.3 | Engage the Public: Poll Question Generation for Social Media Posts | https://aclanthology.org/2021.acl-long.3/ | [
"Zexin Lu",
"Keyang Ding",
"Yuji Zhang",
"Jing Li",
"Baolin Peng",
"Lemao Liu"
] | This paper presents a novel task to generate poll questions for social media posts. It offers an easy way to hear the voice from the public and learn from their feelings to important social topics. While most related work tackles formal languages (e.g., exam papers), we generate poll questions for short and colloquial ... | 2021.acl-long.3 | 10.18653/v1/2021.acl-long.3 | null | null | null |
2021.acl-long.4 | HateCheck: Functional Tests for Hate Speech Detection Models | https://aclanthology.org/2021.acl-long.4/ | [
"Paul Röttger",
"Bertie Vidgen",
"Dong Nguyen",
"Zeerak Waseem",
"Helen Margetts",
"Janet Pierrehumbert"
] | Detecting online hate is a difficult task that even state-of-the-art models struggle with. Typically, hate speech detection models are evaluated by measuring their performance on held-out test data using metrics such as accuracy and F1 score. However, this approach makes it difficult to identify specific model weak poi... | 2021.acl-long.4 | 10.18653/v1/2021.acl-long.4 | null | 2012.15606 | title_snapshot |
2021.acl-long.5 | Unified Dual-view Cognitive Model for Interpretable Claim Verification | https://aclanthology.org/2021.acl-long.5/ | [
"Lianwei Wu",
"Yuan Rao",
"Yuqian Lan",
"Ling Sun",
"Zhaoyin Qi"
] | Recent studies constructing direct interactions between the claim and each single user response (a comment or a relevant article) to capture evidence have shown remarkable success in interpretable claim verification. Owing to different single responses convey different cognition of individual users (i.e., audiences), t... | 2021.acl-long.5 | 10.18653/v1/2021.acl-long.5 | null | 2105.09567 | title_snapshot |
2021.acl-long.6 | DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling | https://aclanthology.org/2021.acl-long.6/ | [
"Lanqing Xue",
"Kaitao Song",
"Duocai Wu",
"Xu Tan",
"Nevin L. Zhang",
"Tao Qin",
"Wei-Qiang Zhang",
"Tie-Yan Liu"
] | Rap generation, which aims to produce lyrics and corresponding singing beats, needs to model both rhymes and rhythms. Previous works for rap generation focused on rhyming lyrics, but ignored rhythmic beats, which are important for rap performance. In this paper, we develop DeepRapper, a Transformer-based rap generation... | 2021.acl-long.6 | 10.18653/v1/2021.acl-long.6 | null | 2107.01875 | title_snapshot |
2021.acl-long.7 | PENS: A Dataset and Generic Framework for Personalized News Headline Generation | https://aclanthology.org/2021.acl-long.7/ | [
"Xiang Ao",
"Xiting Wang",
"Ling Luo",
"Ying Qiao",
"Qing He",
"Xing Xie"
] | In this paper, we formulate the personalized news headline generation problem whose goal is to output a user-specific title based on both a user’s reading interests and a candidate news body to be exposed to her. To build up a benchmark for this problem, we publicize a large-scale dataset named PENS (PErsonalized News ... | 2021.acl-long.7 | 10.18653/v1/2021.acl-long.7 | null | null | null |
2021.acl-long.8 | Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization | https://aclanthology.org/2021.acl-long.8/ | [
"Dongkyu Lee",
"Zhiliang Tian",
"Lanqing Xue",
"Nevin L. Zhang"
] | Text style transfer aims to alter the style (e.g., sentiment) of a sentence while preserving its content. A common approach is to map a given sentence to content representation that is free of style, and the content representation is fed to a decoder with a target style. Previous methods in filtering style completely r... | 2021.acl-long.8 | 10.18653/v1/2021.acl-long.8 | null | 2108.00449 | title_snapshot |
2021.acl-long.9 | Mention Flags (MF): Constraining Transformer-based Text Generators | https://aclanthology.org/2021.acl-long.9/ | [
"Yufei Wang",
"Ian Wood",
"Stephen Wan",
"Mark Dras",
"Mark Johnson"
] | This paper focuses on Seq2Seq (S2S) constrained text generation where the text generator is constrained to mention specific words which are inputs to the encoder in the generated outputs. Pre-trained S2S models or a Copy Mechanism are trained to copy the surface tokens from encoders to decoders, but they cannot guarant... | 2021.acl-long.9 | 10.18653/v1/2021.acl-long.9 | null | null | null |
2021.acl-long.10 | Generalising Multilingual Concept-to-Text NLG with Language Agnostic Delexicalisation | https://aclanthology.org/2021.acl-long.10/ | [
"Giulio Zhou",
"Gerasimos Lampouras"
] | Concept-to-text Natural Language Generation is the task of expressing an input meaning representation in natural language. Previous approaches in this task have been able to generalise to rare or unseen instances by relying on a delexicalisation of the input. However, this often requires that the input appears verbatim... | 2021.acl-long.10 | 10.18653/v1/2021.acl-long.10 | null | 2105.03432 | title_snapshot |
2021.acl-long.11 | Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances | https://aclanthology.org/2021.acl-long.11/ | [
"Zekang Li",
"Jinchao Zhang",
"Zhengcong Fei",
"Yang Feng",
"Jie Zhou"
] | Nowadays, open-domain dialogue models can generate acceptable responses according to the historical context based on the large-scale pre-trained language models. However, they generally concatenate the dialogue history directly as the model input to predict the response, which we named as the flat pattern and ignores t... | 2021.acl-long.11 | 10.18653/v1/2021.acl-long.11 | null | 2106.02227 | title_snapshot |
2021.acl-long.12 | Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking | https://aclanthology.org/2021.acl-long.12/ | [
"Jinyu Guo",
"Kai Shuang",
"Jijie Li",
"Zihan Wang"
] | The goal of dialogue state tracking (DST) is to predict the current dialogue state given all previous dialogue contexts. Existing approaches generally predict the dialogue state at every turn from scratch. However, the overwhelming majority of the slots in each turn should simply inherit the slot values from the previo... | 2021.acl-long.12 | 10.18653/v1/2021.acl-long.12 | null | 2107.12578 | title_snapshot |
2021.acl-long.13 | Transferable Dialogue Systems and User Simulators | https://aclanthology.org/2021.acl-long.13/ | [
"Bo-Hsiang Tseng",
"Yinpei Dai",
"Florian Kreyssig",
"Bill Byrne"
] | One of the difficulties in training dialogue systems is the lack of training data. We explore the possibility of creating dialogue data through the interaction between a dialogue system and a user simulator. Our goal is to develop a modelling framework that can incorporate new dialogue scenarios through self-play betwe... | 2021.acl-long.13 | 10.18653/v1/2021.acl-long.13 | null | 2107.11904 | title_snapshot |
2021.acl-long.14 | BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data | https://aclanthology.org/2021.acl-long.14/ | [
"Haoyu Song",
"Yan Wang",
"Kaiyan Zhang",
"Wei-Nan Zhang",
"Ting Liu"
] | Maintaining a consistent persona is essential for dialogue agents. Although tremendous advancements have been brought, the limited-scale of annotated personalized dialogue datasets is still a barrier towards training robust and consistent persona-based dialogue models. This work shows how this challenge can be addresse... | 2021.acl-long.14 | 10.18653/v1/2021.acl-long.14 | null | 2106.06169 | title_snapshot |
2021.acl-long.15 | GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling | https://aclanthology.org/2021.acl-long.15/ | [
"Libo Qin",
"Fuxuan Wei",
"Tianbao Xie",
"Xiao Xu",
"Wanxiang Che",
"Ting Liu"
] | Multi-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention. However, the state-of-the-art joint models heavily rely on autoregressive approaches, resulting in two issues: slow inference speed and information leakage. In this paper, we explore a non-autoregressive model for jo... | 2021.acl-long.15 | 10.18653/v1/2021.acl-long.15 | null | 2106.01925 | title_snapshot |
2021.acl-long.16 | Accelerating BERT Inference for Sequence Labeling via Early-Exit | https://aclanthology.org/2021.acl-long.16/ | [
"Xiaonan Li",
"Yunfan Shao",
"Tianxiang Sun",
"Hang Yan",
"Xipeng Qiu",
"Xuanjing Huang"
] | Both performance and efficiency are crucial factors for sequence labeling tasks in many real-world scenarios. Although the pre-trained models (PTMs) have significantly improved the performance of various sequence labeling tasks, their computational cost is expensive. To alleviate this problem, we extend the recent succ... | 2021.acl-long.16 | 10.18653/v1/2021.acl-long.16 | null | 2105.13878 | title_snapshot |
2021.acl-long.17 | Modularized Interaction Network for Named Entity Recognition | https://aclanthology.org/2021.acl-long.17/ | [
"Fei Li",
"Zheng Wang",
"Siu Cheung Hui",
"Lejian Liao",
"Dandan Song",
"Jing Xu",
"Guoxiu He",
"Meihuizi Jia"
] | Although the existing Named Entity Recognition (NER) models have achieved promising performance, they suffer from certain drawbacks. The sequence labeling-based NER models do not perform well in recognizing long entities as they focus only on word-level information, while the segment-based NER models which focus on pro... | 2021.acl-long.17 | 10.18653/v1/2021.acl-long.17 | null | null | null |
2021.acl-long.18 | Capturing Event Argument Interaction via A Bi-Directional Entity-Level Recurrent Decoder | https://aclanthology.org/2021.acl-long.18/ | [
"Xi Xiangyu",
"Wei Ye",
"Shikun Zhang",
"Quanxiu Wang",
"Huixing Jiang",
"Wei Wu"
] | Capturing interactions among event arguments is an essential step towards robust event argument extraction (EAE). However, existing efforts in this direction suffer from two limitations: 1) The argument role type information of contextual entities is mainly utilized as training signals, ignoring the potential merits of... | 2021.acl-long.18 | 10.18653/v1/2021.acl-long.18 | null | 2107.00189 | title_snapshot |
2021.acl-long.19 | UniRE: A Unified Label Space for Entity Relation Extraction | https://aclanthology.org/2021.acl-long.19/ | [
"Yijun Wang",
"Changzhi Sun",
"Yuanbin Wu",
"Hao Zhou",
"Lei Li",
"Junchi Yan"
] | Many joint entity relation extraction models setup two separated label spaces for the two sub-tasks (i.e., entity detection and relation classification). We argue that this setting may hinder the information interaction between entities and relations. In this work, we propose to eliminate the different treatment on the... | 2021.acl-long.19 | 10.18653/v1/2021.acl-long.19 | null | 2107.04292 | title_snapshot |
2021.acl-long.20 | Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction | https://aclanthology.org/2021.acl-long.20/ | [
"Li Cui",
"Deqing Yang",
"Jiaxin Yu",
"Chengwei Hu",
"Jiayang Cheng",
"Jingjie Yi",
"Yanghua Xiao"
] | Continual learning has gained increasing attention in recent years, thanks to its biological interpretation and efficiency in many real-world applications. As a typical task of continual learning, continual relation extraction (CRE) aims to extract relations between entities from texts, where the samples of different r... | 2021.acl-long.20 | 10.18653/v1/2021.acl-long.20 | null | null | null |
2021.acl-long.21 | Contrastive Learning for Many-to-many Multilingual Neural Machine Translation | https://aclanthology.org/2021.acl-long.21/ | [
"Xiao Pan",
"Mingxuan Wang",
"Liwei Wu",
"Lei Li"
] | Existing multilingual machine translation approaches mainly focus on English-centric directions, while the non-English directions still lag behind. In this work, we aim to build a many-to-many translation system with an emphasis on the quality of non-English language directions. Our intuition is based on the hypothesis... | 2021.acl-long.21 | 10.18653/v1/2021.acl-long.21 | null | 2105.09501 | title_snapshot |
2021.acl-long.22 | Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation | https://aclanthology.org/2021.acl-long.22/ | [
"Mathias Müller",
"Rico Sennrich"
] | Neural Machine Translation (NMT) currently exhibits biases such as producing translations that are too short and overgenerating frequent words, and shows poor robustness to copy noise in training data or domain shift. Recent work has tied these shortcomings to beam search – the de facto standard inference algorithm in ... | 2021.acl-long.22 | 10.18653/v1/2021.acl-long.22 | null | 2105.08504 | title_snapshot |
2021.acl-long.23 | Multi-Head Highly Parallelized LSTM Decoder for Neural Machine Translation | https://aclanthology.org/2021.acl-long.23/ | [
"Hongfei Xu",
"Qiuhui Liu",
"Josef van Genabith",
"Deyi Xiong",
"Meng Zhang"
] | One of the reasons Transformer translation models are popular is that self-attention networks for context modelling can be easily parallelized at sequence level. However, the computational complexity of a self-attention network is O(n^2), increasing quadratically with sequence length. By contrast, the complexity of LST... | 2021.acl-long.23 | 10.18653/v1/2021.acl-long.23 | null | null | null |
2021.acl-long.24 | A Bidirectional Transformer Based Alignment Model for Unsupervised Word Alignment | https://aclanthology.org/2021.acl-long.24/ | [
"Jingyi Zhang",
"Josef van Genabith"
] | Word alignment and machine translation are two closely related tasks. Neural translation models, such as RNN-based and Transformer models, employ a target-to-source attention mechanism which can provide rough word alignments, but with a rather low accuracy. High-quality word alignment can help neural machine translatio... | 2021.acl-long.24 | 10.18653/v1/2021.acl-long.24 | null | null | null |
2021.acl-long.25 | Learning Language Specific Sub-network for Multilingual Machine Translation | https://aclanthology.org/2021.acl-long.25/ | [
"Zehui Lin",
"Liwei Wu",
"Mingxuan Wang",
"Lei Li"
] | Multilingual neural machine translation aims at learning a single translation model for multiple languages. These jointly trained models often suffer from performance degradationon rich-resource language pairs. We attribute this degeneration to parameter interference. In this paper, we propose LaSS to jointly train a s... | 2021.acl-long.25 | 10.18653/v1/2021.acl-long.25 | null | 2105.09259 | title_snapshot |
2021.acl-long.26 | Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis | https://aclanthology.org/2021.acl-long.26/ | [
"Linyi Yang",
"Jiazheng Li",
"Padraig Cunningham",
"Yue Zhang",
"Barry Smyth",
"Ruihai Dong"
] | While state-of-the-art NLP models have been achieving the excellent performance of a wide range of tasks in recent years, important questions are being raised about their robustness and their underlying sensitivity to systematic biases that may exist in their training and test data. Such issues come to be manifest in p... | 2021.acl-long.26 | 10.18653/v1/2021.acl-long.26 | null | 2106.15231 | title_snapshot |
2021.acl-long.27 | Bridge-Based Active Domain Adaptation for Aspect Term Extraction | https://aclanthology.org/2021.acl-long.27/ | [
"Zhuang Chen",
"Tieyun Qian"
] | As a fine-grained task, the annotation cost of aspect term extraction is extremely high. Recent attempts alleviate this issue using domain adaptation that transfers common knowledge across domains. Since most aspect terms are domain-specific, they cannot be transferred directly. Existing methods solve this problem by a... | 2021.acl-long.27 | 10.18653/v1/2021.acl-long.27 | null | null | null |
2021.acl-long.28 | Multimodal Sentiment Detection Based on Multi-channel Graph Neural Networks | https://aclanthology.org/2021.acl-long.28/ | [
"Xiaocui Yang",
"Shi Feng",
"Yifei Zhang",
"Daling Wang"
] | With the popularity of smartphones, we have witnessed the rapid proliferation of multimodal posts on various social media platforms. We observe that the multimodal sentiment expression has specific global characteristics, such as the interdependencies of objects or scenes within the image. However, most previous studie... | 2021.acl-long.28 | 10.18653/v1/2021.acl-long.28 | null | null | null |
2021.acl-long.29 | Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions | https://aclanthology.org/2021.acl-long.29/ | [
"Hongjie Cai",
"Rui Xia",
"Jianfei Yu"
] | Product reviews contain a large number of implicit aspects and implicit opinions. However, most of the existing studies in aspect-based sentiment analysis ignored this problem. In this work, we introduce a new task, named Aspect-Category-Opinion-Sentiment (ACOS) Quadruple Extraction, with the goal to extract all aspect... | 2021.acl-long.29 | 10.18653/v1/2021.acl-long.29 | null | null | null |
2021.acl-long.30 | PASS: Perturb-and-Select Summarizer for Product Reviews | https://aclanthology.org/2021.acl-long.30/ | [
"Nadav Oved",
"Ran Levy"
] | The product reviews summarization task aims to automatically produce a short summary for a set of reviews of a given product. Such summaries are expected to aggregate a range of different opinions in a concise, coherent and informative manner. This challenging task gives rise to two shortcomings in existing work. First... | 2021.acl-long.30 | 10.18653/v1/2021.acl-long.30 | null | null | null |
2021.acl-long.31 | Deep Differential Amplifier for Extractive Summarization | https://aclanthology.org/2021.acl-long.31/ | [
"Ruipeng Jia",
"Yanan Cao",
"Fang Fang",
"Yuchen Zhou",
"Zheng Fang",
"Yanbing Liu",
"Shi Wang"
] | For sentence-level extractive summarization, there is a disproportionate ratio of selected and unselected sentences, leading to flatting the summary features when maximizing the accuracy. The imbalanced classification of summarization is inherent, which can’t be addressed by common algorithms easily. In this paper, we ... | 2021.acl-long.31 | 10.18653/v1/2021.acl-long.31 | null | null | null |
2021.acl-long.32 | Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization by Generating Multiple Summaries | https://aclanthology.org/2021.acl-long.32/ | [
"Yi Yu",
"Adam Jatowt",
"Antoine Doucet",
"Kazunari Sugiyama",
"Masatoshi Yoshikawa"
] | In this paper, we address a novel task, Multiple TimeLine Summarization (MTLS), which extends the flexibility and versatility of Time-Line Summarization (TLS). Given any collection of time-stamped news articles, MTLS automatically discovers important yet different stories and generates a corresponding time-line for eac... | 2021.acl-long.32 | 10.18653/v1/2021.acl-long.32 | null | null | null |
2021.acl-long.33 | Self-Supervised Multimodal Opinion Summarization | https://aclanthology.org/2021.acl-long.33/ | [
"Jinbae Im",
"Moonki Kim",
"Hoyeop Lee",
"Hyunsouk Cho",
"Sehee Chung"
] | Recently, opinion summarization, which is the generation of a summary from multiple reviews, has been conducted in a self-supervised manner by considering a sampled review as a pseudo summary. However, non-text data such as image and metadata related to reviews have been considered less often. To use the abundant infor... | 2021.acl-long.33 | 10.18653/v1/2021.acl-long.33 | null | 2105.13135 | title_snapshot |
2021.acl-long.34 | A Training-free and Reference-free Summarization Evaluation Metric via Centrality-weighted Relevance and Self-referenced Redundancy | https://aclanthology.org/2021.acl-long.34/ | [
"Wang Chen",
"Piji Li",
"Irwin King"
] | In recent years, reference-based and supervised summarization evaluation metrics have been widely explored. However, collecting human-annotated references and ratings are costly and time-consuming. To avoid these limitations, we propose a training-free and reference-free summarization evaluation metric. Our metric cons... | 2021.acl-long.34 | 10.18653/v1/2021.acl-long.34 | null | 2106.13945 | title_snapshot |
2021.acl-long.35 | DESCGEN: A Distantly Supervised Datasetfor Generating Entity Descriptions | https://aclanthology.org/2021.acl-long.35/ | [
"Weijia Shi",
"Mandar Joshi",
"Luke Zettlemoyer"
] | Short textual descriptions of entities provide summaries of their key attributes and have been shown to be useful sources of background knowledge for tasks such as entity linking and question answering. However, generating entity descriptions, especially for new and long-tail entities, can be challenging since relevant... | 2021.acl-long.35 | 10.18653/v1/2021.acl-long.35 | null | 2106.05365 | title_judge |
2021.acl-long.36 | Introducing Orthogonal Constraint in Structural Probes | https://aclanthology.org/2021.acl-long.36/ | [
"Tomasz Limisiewicz",
"David Mareček"
] | With the recent success of pre-trained models in NLP, a significant focus was put on interpreting their representations. One of the most prominent approaches is structural probing (Hewitt and Manning, 2019), where a linear projection of word embeddings is performed in order to approximate the topology of dependency str... | 2021.acl-long.36 | 10.18653/v1/2021.acl-long.36 | null | 2012.15228 | title_snapshot |
2021.acl-long.37 | Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger | https://aclanthology.org/2021.acl-long.37/ | [
"Fanchao Qi",
"Mukai Li",
"Yangyi Chen",
"Zhengyan Zhang",
"Zhiyuan Liu",
"Yasheng Wang",
"Maosong Sun"
] | Backdoor attacks are a kind of insidious security threat against machine learning models. After being injected with a backdoor in training, the victim model will produce adversary-specified outputs on the inputs embedded with predesigned triggers but behave properly on normal inputs during inference. As a sort of emerg... | 2021.acl-long.37 | 10.18653/v1/2021.acl-long.37 | null | 2105.12400 | title_snapshot |
2021.acl-long.38 | Examining the Inductive Bias of Neural Language Models with Artificial Languages | https://aclanthology.org/2021.acl-long.38/ | [
"Jennifer C. White",
"Ryan Cotterell"
] | Since language models are used to model a wide variety of languages, it is natural to ask whether the neural architectures used for the task have inductive biases towards modeling particular types of languages. Investigation of these biases has proved complicated due to the many variables that appear in the experimenta... | 2021.acl-long.38 | 10.18653/v1/2021.acl-long.38 | null | 2106.01044 | title_snapshot |
2021.acl-long.39 | Explaining Contextualization in Language Models using Visual Analytics | https://aclanthology.org/2021.acl-long.39/ | [
"Rita Sevastjanova",
"Aikaterini-Lida Kalouli",
"Christin Beck",
"Hanna Schäfer",
"Mennatallah El-Assady"
] | Despite the success of contextualized language models on various NLP tasks, it is still unclear what these models really learn. In this paper, we contribute to the current efforts of explaining such models by exploring the continuum between function and content words with respect to contextualization in BERT, based on ... | 2021.acl-long.39 | 10.18653/v1/2021.acl-long.39 | null | null | null |
2021.acl-long.40 | Improving the Faithfulness of Attention-based Explanations with Task-specific Information for Text Classification | https://aclanthology.org/2021.acl-long.40/ | [
"George Chrysostomou",
"Nikolaos Aletras"
] | Neural network architectures in natural language processing often use attention mechanisms to produce probability distributions over input token representations. Attention has empirically been demonstrated to improve performance in various tasks, while its weights have been extensively used as explanations for model pr... | 2021.acl-long.40 | 10.18653/v1/2021.acl-long.40 | null | 2105.02657 | title_snapshot |
2021.acl-long.41 | Generating Landmark Navigation Instructions from Maps as a Graph-to-Text Problem | https://aclanthology.org/2021.acl-long.41/ | [
"Raphael Schumann",
"Stefan Riezler"
] | Car-focused navigation services are based on turns and distances of named streets, whereas navigation instructions naturally used by humans are centered around physical objects called landmarks. We present a neural model that takes OpenStreetMap representations as input and learns to generate navigation instructions th... | 2021.acl-long.41 | 10.18653/v1/2021.acl-long.41 | null | 2012.15329 | title_snapshot |
2021.acl-long.42 | E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning | https://aclanthology.org/2021.acl-long.42/ | [
"Haiyang Xu",
"Ming Yan",
"Chenliang Li",
"Bin Bi",
"Songfang Huang",
"Wenming Xiao",
"Fei Huang"
] | Vision-language pre-training (VLP) on large-scale image-text pairs has achieved huge success for the cross-modal downstream tasks. The most existing pre-training methods mainly adopt a two-step training procedure, which firstly employs a pre-trained object detector to extract region-based visual features, then concaten... | 2021.acl-long.42 | 10.18653/v1/2021.acl-long.42 | null | 2106.01804 | title_snapshot |
2021.acl-long.43 | Learning Relation Alignment for Calibrated Cross-modal Retrieval | https://aclanthology.org/2021.acl-long.43/ | [
"Shuhuai Ren",
"Junyang Lin",
"Guangxiang Zhao",
"Rui Men",
"An Yang",
"Jingren Zhou",
"Xu Sun",
"Hongxia Yang"
] | Despite the achievements of large-scale multimodal pre-training approaches, cross-modal retrieval, e.g., image-text retrieval, remains a challenging task. To bridge the semantic gap between the two modalities, previous studies mainly focus on word-region alignment at the object level, lacking the matching between the l... | 2021.acl-long.43 | 10.18653/v1/2021.acl-long.43 | null | 2105.13868 | title_snapshot |
2021.acl-long.44 | KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense Generation | https://aclanthology.org/2021.acl-long.44/ | [
"Yiran Xing",
"Zai Shi",
"Zhao Meng",
"Gerhard Lakemeyer",
"Yunpu Ma",
"Roger Wattenhofer"
] | We present Knowledge Enhanced Multimodal BART (KM-BART), which is a Transformer-based sequence-to-sequence model capable of reasoning about commonsense knowledge from multimodal inputs of images and texts. We adapt the generative BART architecture (Lewis et al., 2020) to a multimodal model with visual and textual input... | 2021.acl-long.44 | 10.18653/v1/2021.acl-long.44 | null | 2101.00419 | title_snapshot |
2021.acl-long.45 | Cascaded Head-colliding Attention | https://aclanthology.org/2021.acl-long.45/ | [
"Lin Zheng",
"Zhiyong Wu",
"Lingpeng Kong"
] | Transformers have advanced the field of natural language processing (NLP) on a variety of important tasks. At the cornerstone of the Transformer architecture is the multi-head attention (MHA) mechanism which models pairwise interactions between the elements of the sequence. Despite its massive success, the current fram... | 2021.acl-long.45 | 10.18653/v1/2021.acl-long.45 | null | 2105.14850 | title_snapshot |
2021.acl-long.46 | Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor | https://aclanthology.org/2021.acl-long.46/ | [
"Xinyu Wang",
"Yong Jiang",
"Zhaohui Yan",
"Zixia Jia",
"Nguyen Bach",
"Tao Wang",
"Zhongqiang Huang",
"Fei Huang",
"Kewei Tu"
] | Knowledge distillation is a critical technique to transfer knowledge between models, typically from a large model (the teacher) to a more fine-grained one (the student). The objective function of knowledge distillation is typically the cross-entropy between the teacher and the student’s output distributions. However, f... | 2021.acl-long.46 | 10.18653/v1/2021.acl-long.46 | null | 2010.05010 | title_snapshot |
2021.acl-long.47 | Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks | https://aclanthology.org/2021.acl-long.47/ | [
"Rabeeh Karimi Mahabadi",
"Sebastian Ruder",
"Mostafa Dehghani",
"James Henderson"
] | State-of-the-art parameter-efficient fine-tuning methods rely on introducing adapter modules between the layers of a pretrained language model. However, such modules are trained separately for each task and thus do not enable sharing information across tasks. In this paper, we show that we can learn adapter parameters ... | 2021.acl-long.47 | 10.18653/v1/2021.acl-long.47 | null | 2106.04489 | title_snapshot |
2021.acl-long.48 | COSY: COunterfactual SYntax for Cross-Lingual Understanding | https://aclanthology.org/2021.acl-long.48/ | [
"Sicheng Yu",
"Hao Zhang",
"Yulei Niu",
"Qianru Sun",
"Jing Jiang"
] | Pre-trained multilingual language models, e.g., multilingual-BERT, are widely used in cross-lingual tasks, yielding the state-of-the-art performance. However, such models suffer from a large performance gap between source and target languages, especially in the zero-shot setting, where the models are fine-tuned only on... | 2021.acl-long.48 | 10.18653/v1/2021.acl-long.48 | null | null | null |
2021.acl-long.49 | OoMMix: Out-of-manifold Regularization in Contextual Embedding Space for Text Classification | https://aclanthology.org/2021.acl-long.49/ | [
"Seonghyeon Lee",
"Dongha Lee",
"Hwanjo Yu"
] | Recent studies on neural networks with pre-trained weights (i.e., BERT) have mainly focused on a low-dimensional subspace, where the embedding vectors computed from input words (or their contexts) are located. In this work, we propose a new approach, called OoMMix, to finding and regularizing the remainder of the space... | 2021.acl-long.49 | 10.18653/v1/2021.acl-long.49 | null | 2105.06750 | title_judge |
2021.acl-long.50 | Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model | https://aclanthology.org/2021.acl-long.50/ | [
"Kathleen C. Fraser",
"Isar Nejadgholi",
"Svetlana Kiritchenko"
] | Stereotypical language expresses widely-held beliefs about different social categories. Many stereotypes are overtly negative, while others may appear positive on the surface, but still lead to negative consequences. In this work, we present a computational approach to interpreting stereotypes in text through the Stere... | 2021.acl-long.50 | 10.18653/v1/2021.acl-long.50 | null | 2106.02596 | title_snapshot |
2021.acl-long.51 | Structurizing Misinformation Stories via Rationalizing Fact-Checks | https://aclanthology.org/2021.acl-long.51/ | [
"Shan Jiang",
"Christo Wilson"
] | Misinformation has recently become a well-documented matter of public concern. Existing studies on this topic have hitherto adopted a coarse concept of misinformation, which incorporates a broad spectrum of story types ranging from political conspiracies to misinterpreted pranks. This paper aims to structurize these mi... | 2021.acl-long.51 | 10.18653/v1/2021.acl-long.51 | null | null | null |
2021.acl-long.52 | Modeling Language Usage and Listener Engagement in Podcasts | https://aclanthology.org/2021.acl-long.52/ | [
"Sravana Reddy",
"Mariya Lazarova",
"Yongze Yu",
"Rosie Jones"
] | While there is an abundance of advice to podcast creators on how to speak in ways that engage their listeners, there has been little data-driven analysis of podcasts that relates linguistic style with engagement. In this paper, we investigate how various factors – vocabulary diversity, distinctiveness, emotion, and syn... | 2021.acl-long.52 | 10.18653/v1/2021.acl-long.52 | null | 2106.06605 | title_snapshot |
2021.acl-long.53 | Breaking Down the Invisible Wall of Informal Fallacies in Online Discussions | https://aclanthology.org/2021.acl-long.53/ | [
"Saumya Sahai",
"Oana Balalau",
"Roxana Horincar"
] | People debate on a variety of topics on online platforms such as Reddit, or Facebook. Debates can be lengthy, with users exchanging a wealth of information and opinions. However, conversations do not always go smoothly, and users sometimes engage in unsound argumentation techniques to prove a claim. These techniques ar... | 2021.acl-long.53 | 10.18653/v1/2021.acl-long.53 | null | null | null |
2021.acl-long.54 | SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues | https://aclanthology.org/2021.acl-long.54/ | [
"Liang Qiu",
"Yuan Liang",
"Yizhou Zhao",
"Pan Lu",
"Baolin Peng",
"Zhou Yu",
"Ying Nian Wu",
"Song-Chun Zhu"
] | Inferring social relations from dialogues is vital for building emotionally intelligent robots to interpret human language better and act accordingly. We model the social network as an And-or Graph, named SocAoG, for the consistency of relations among a group and leveraging attributes as inference cues. Moreover, we fo... | 2021.acl-long.54 | 10.18653/v1/2021.acl-long.54 | null | 2106.01006 | title_snapshot |
2021.acl-long.55 | TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systems | https://aclanthology.org/2021.acl-long.55/ | [
"Bill Byrne",
"Karthik Krishnamoorthi",
"Saravanan Ganesh",
"Mihir Kale"
] | We present a data-driven, end-to-end approach to transaction-based dialog systems that performs at near-human levels in terms of verbal response quality and factual grounding accuracy. We show that two essential components of the system produce these results: a sufficiently large and diverse, in-domain labeled dataset,... | 2021.acl-long.55 | 10.18653/v1/2021.acl-long.55 | null | 2012.12458 | title_snapshot |
2021.acl-long.56 | Improving Dialog Systems for Negotiation with Personality Modeling | https://aclanthology.org/2021.acl-long.56/ | [
"Runzhe Yang",
"Jingxiao Chen",
"Karthik Narasimhan"
] | In this paper, we explore the ability to model and infer personality types of opponents, predict their responses, and use this information to adapt a dialog agent’s high-level strategy in negotiation tasks. Inspired by the idea of incorporating a theory of mind (ToM) into machines, we introduce a probabilistic formulat... | 2021.acl-long.56 | 10.18653/v1/2021.acl-long.56 | null | 2010.09954 | title_snapshot |
2021.acl-long.57 | Learning from Perturbations: Diverse and Informative Dialogue Generation with Inverse Adversarial Training | https://aclanthology.org/2021.acl-long.57/ | [
"Wangchunshu Zhou",
"Qifei Li",
"Chenle Li"
] | In this paper, we propose Inverse Adversarial Training (IAT) algorithm for training neural dialogue systems to avoid generic responses and model dialogue history better. In contrast to standard adversarial training algorithms, IAT encourages the model to be sensitive to the perturbation in the dialogue history and ther... | 2021.acl-long.57 | 10.18653/v1/2021.acl-long.57 | null | 2105.15171 | title_snapshot |
2021.acl-long.58 | Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features | https://aclanthology.org/2021.acl-long.58/ | [
"Hannah Rashkin",
"David Reitter",
"Gaurav Singh Tomar",
"Dipanjan Das"
] | Knowledge-grounded dialogue systems are intended to convey information that is based on evidence provided in a given source text. We discuss the challenges of training a generative neural dialogue model for such systems that is controlled to stay faithful to the evidence. Existing datasets contain a mix of conversation... | 2021.acl-long.58 | 10.18653/v1/2021.acl-long.58 | null | 2107.06963 | title_snapshot |
2021.acl-long.59 | CitationIE: Leveraging the Citation Graph for Scientific Information Extraction | https://aclanthology.org/2021.acl-long.59/ | [
"Vijay Viswanathan",
"Graham Neubig",
"Pengfei Liu"
] | Automatically extracting key information from scientific documents has the potential to help scientists work more efficiently and accelerate the pace of scientific progress. Prior work has considered extracting document-level entity clusters and relations end-to-end from raw scientific text, which can improve literatur... | 2021.acl-long.59 | 10.18653/v1/2021.acl-long.59 | null | 2106.01560 | title_snapshot |
2021.acl-long.60 | From Discourse to Narrative: Knowledge Projection for Event Relation Extraction | https://aclanthology.org/2021.acl-long.60/ | [
"Jialong Tang",
"Hongyu Lin",
"Meng Liao",
"Yaojie Lu",
"Xianpei Han",
"Le Sun",
"Weijian Xie",
"Jin Xu"
] | Current event-centric knowledge graphs highly rely on explicit connectives to mine relations between events. Unfortunately, due to the sparsity of connectives, these methods severely undermine the coverage of EventKGs. The lack of high-quality labelled corpora further exacerbates that problem. In this paper, we propose... | 2021.acl-long.60 | 10.18653/v1/2021.acl-long.60 | null | 2106.08629 | title_snapshot |
2021.acl-long.61 | AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER | https://aclanthology.org/2021.acl-long.61/ | [
"Weile Chen",
"Huiqiang Jiang",
"Qianhui Wu",
"Börje F. Karlsson",
"Yi Guan"
] | Neural methods have been shown to achieve high performance in Named Entity Recognition (NER), but rely on costly high-quality labeled data for training, which is not always available across languages. While previous works have shown that unlabeled data in a target language can be used to improve cross-lingual model per... | 2021.acl-long.61 | 10.18653/v1/2021.acl-long.61 | null | 2106.02300 | title_snapshot |
2021.acl-long.62 | Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge | https://aclanthology.org/2021.acl-long.62/ | [
"Linmei Hu",
"Tianchi Yang",
"Luhao Zhang",
"Wanjun Zhong",
"Duyu Tang",
"Chuan Shi",
"Nan Duan",
"Ming Zhou"
] | Nowadays, fake news detection, which aims to verify whether a news document is trusted or fake, has become urgent and important. Most existing methods rely heavily on linguistic and semantic features from the news content, and fail to effectively exploit external knowledge which could help determine whether the news do... | 2021.acl-long.62 | 10.18653/v1/2021.acl-long.62 | null | null | null |
2021.acl-long.63 | Discontinuous Named Entity Recognition as Maximal Clique Discovery | https://aclanthology.org/2021.acl-long.63/ | [
"Yucheng Wang",
"Bowen Yu",
"Hongsong Zhu",
"Tingwen Liu",
"Nan Yu",
"Limin Sun"
] | Named entity recognition (NER) remains challenging when entity mentions can be discontinuous. Existing methods break the recognition process into several sequential steps. In training, they predict conditioned on the golden intermediate results, while at inference relying on the model output of the previous steps, whic... | 2021.acl-long.63 | 10.18653/v1/2021.acl-long.63 | null | 2106.00218 | title_snapshot |
2021.acl-long.64 | LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking | https://aclanthology.org/2021.acl-long.64/ | [
"Hang Jiang",
"Sairam Gurajada",
"Qiuhao Lu",
"Sumit Neelam",
"Lucian Popa",
"Prithviraj Sen",
"Yunyao Li",
"Alexander Gray"
] | Entity linking (EL) is the task of disambiguating mentions appearing in text by linking them to entities in a knowledge graph, a crucial task for text understanding, question answering or conversational systems. In the special case of short-text EL, which poses additional challenges due to limited context, prior approa... | 2021.acl-long.64 | 10.18653/v1/2021.acl-long.64 | null | 2106.09795 | title_snapshot |
2021.acl-long.65 | Do Context-Aware Translation Models Pay the Right Attention? | https://aclanthology.org/2021.acl-long.65/ | [
"Kayo Yin",
"Patrick Fernandes",
"Danish Pruthi",
"Aditi Chaudhary",
"André F. T. Martins",
"Graham Neubig"
] | Context-aware machine translation models are designed to leverage contextual information, but often fail to do so. As a result, they inaccurately disambiguate pronouns and polysemous words that require context for resolution. In this paper, we ask several questions: What contexts do human translators use to resolve amb... | 2021.acl-long.65 | 10.18653/v1/2021.acl-long.65 | null | 2105.06977 | title_snapshot |
2021.acl-long.66 | Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data | https://aclanthology.org/2021.acl-long.66/ | [
"Wei-Jen Ko",
"Ahmed El-Kishky",
"Adithya Renduchintala",
"Vishrav Chaudhary",
"Naman Goyal",
"Francisco Guzmán",
"Pascale Fung",
"Philipp Koehn",
"Mona Diab"
] | The scarcity of parallel data is a major obstacle for training high-quality machine translation systems for low-resource languages. Fortunately, some low-resource languages are linguistically related or similar to high-resource languages; these related languages may share many lexical or syntactic structures. In this w... | 2021.acl-long.66 | 10.18653/v1/2021.acl-long.66 | null | 2105.15071 | title_snapshot |
2021.acl-long.67 | Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment | https://aclanthology.org/2021.acl-long.67/ | [
"Haoyue Shi",
"Luke Zettlemoyer",
"Sida I. Wang"
] | Bilingual lexicons map words in one language to their translations in another, and are typically induced by learning linear projections to align monolingual word embedding spaces. In this paper, we show it is possible to produce much higher quality lexicons with methods that combine (1) unsupervised bitext mining and (... | 2021.acl-long.67 | 10.18653/v1/2021.acl-long.67 | null | 2101.00148 | title_snapshot |
2021.acl-long.68 | Multilingual Speech Translation from Efficient Finetuning of Pretrained Models | https://aclanthology.org/2021.acl-long.68/ | [
"Xian Li",
"Changhan Wang",
"Yun Tang",
"Chau Tran",
"Yuqing Tang",
"Juan Pino",
"Alexei Baevski",
"Alexis Conneau",
"Michael Auli"
] | We present a simple yet effective approach to build multilingual speech-to-text (ST) translation through efficient transfer learning from a pretrained speech encoder and text decoder. Our key finding is that a minimalistic LNA (LayerNorm and Attention) finetuning can achieve zero-shot crosslingual and cross-modality tr... | 2021.acl-long.68 | 10.18653/v1/2021.acl-long.68 | null | 2010.12829 | title_judge |
2021.acl-long.69 | Learning Faithful Representations of Causal Graphs | https://aclanthology.org/2021.acl-long.69/ | [
"Ananth Balashankar",
"Lakshminarayanan Subramanian"
] | Learning contextual text embeddings that represent causal graphs has been useful in improving the performance of downstream tasks like causal treatment effect estimation. However, existing causal embeddings which are trained to predict direct causal links, fail to capture other indirect causal links of the graph, thus ... | 2021.acl-long.69 | 10.18653/v1/2021.acl-long.69 | null | null | null |
2021.acl-long.70 | What Context Features Can Transformer Language Models Use? | https://aclanthology.org/2021.acl-long.70/ | [
"Joe O’Connor",
"Jacob Andreas"
] | Transformer-based language models benefit from conditioning on contexts of hundreds to thousands of previous tokens. What aspects of these contexts contribute to accurate model prediction? We describe a series of experiments that measure usable information by selectively ablating lexical and structural information in t... | 2021.acl-long.70 | 10.18653/v1/2021.acl-long.70 | null | 2106.08367 | title_snapshot |
2021.acl-long.71 | Integrated Directional Gradients: Feature Interaction Attribution for Neural NLP Models | https://aclanthology.org/2021.acl-long.71/ | [
"Sandipan Sikdar",
"Parantapa Bhattacharya",
"Kieran Heese"
] | In this paper, we introduce Integrated Directional Gradients (IDG), a method for attributing importance scores to groups of features, indicating their relevance to the output of a neural network model for a given input. The success of Deep Neural Networks has been attributed to their ability to capture higher level fea... | 2021.acl-long.71 | 10.18653/v1/2021.acl-long.71 | null | null | null |
2021.acl-long.72 | DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations | https://aclanthology.org/2021.acl-long.72/ | [
"John Giorgi",
"Osvald Nitski",
"Bo Wang",
"Gary Bader"
] | Sentence embeddings are an important component of many natural language processing (NLP) systems. Like word embeddings, sentence embeddings are typically learned on large text corpora and then transferred to various downstream tasks, such as clustering and retrieval. Unlike word embeddings, the highest performing solut... | 2021.acl-long.72 | 10.18653/v1/2021.acl-long.72 | null | 2006.03659 | title_snapshot |
2021.acl-long.73 | XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation | https://aclanthology.org/2021.acl-long.73/ | [
"Dongqin Xu",
"Junhui Li",
"Muhua Zhu",
"Min Zhang",
"Guodong Zhou"
] | Due to the scarcity of annotated data, Abstract Meaning Representation (AMR) research is relatively limited and challenging for languages other than English. Upon the availability of English AMR dataset and English-to- X parallel datasets, in this paper we propose a novel cross-lingual pre-training approach via multi-t... | 2021.acl-long.73 | 10.18653/v1/2021.acl-long.73 | null | null | null |
2021.acl-long.74 | Span-based Semantic Parsing for Compositional Generalization | https://aclanthology.org/2021.acl-long.74/ | [
"Jonathan Herzig",
"Jonathan Berant"
] | Despite the success of sequence-to-sequence (seq2seq) models in semantic parsing, recent work has shown that they fail in compositional generalization, i.e., the ability to generalize to new structures built of components observed during training. In this work, we posit that a span-based parser should lead to better co... | 2021.acl-long.74 | 10.18653/v1/2021.acl-long.74 | null | 2009.06040 | title_snapshot |
2021.acl-long.75 | Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both? | https://aclanthology.org/2021.acl-long.75/ | [
"Peter Shaw",
"Ming-Wei Chang",
"Panupong Pasupat",
"Kristina Toutanova"
] | Sequence-to-sequence models excel at handling natural language variation, but have been shown to struggle with out-of-distribution compositional generalization. This has motivated new specialized architectures with stronger compositional biases, but most of these approaches have only been evaluated on synthetically-gen... | 2021.acl-long.75 | 10.18653/v1/2021.acl-long.75 | null | 2010.12725 | title_snapshot |
2021.acl-long.76 | A Targeted Assessment of Incremental Processing in Neural Language Models and Humans | https://aclanthology.org/2021.acl-long.76/ | [
"Ethan Wilcox",
"Pranali Vani",
"Roger Levy"
] | We present a targeted, scaled-up comparison of incremental processing in humans and neural language models by collecting by-word reaction time data for sixteen different syntactic test suites across a range of structural phenomena. Human reaction time data comes from a novel online experimental paradigm called the Inte... | 2021.acl-long.76 | 10.18653/v1/2021.acl-long.76 | null | 2106.03232 | title_judge |
2021.acl-long.77 | The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing | https://aclanthology.org/2021.acl-long.77/ | [
"Valentina Pyatkin",
"Shoval Sadde",
"Aynat Rubinstein",
"Paul Portner",
"Reut Tsarfaty"
] | Modality is the linguistic ability to describe vents with added information such as how desirable, plausible, or feasible they are. Modality is important for many NLP downstream tasks such as the detection of hedging, uncertainty, speculation, and more. Previous studies that address modality detection in NLP often rest... | 2021.acl-long.77 | 10.18653/v1/2021.acl-long.77 | null | 2106.08037 | title_snapshot |
2021.acl-long.78 | To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings | https://aclanthology.org/2021.acl-long.78/ | [
"Sarah Moeller",
"Ling Liu",
"Mans Hulden"
] | Part-of-Speech (POS) tags are routinely included as features in many NLP tasks. However, the importance and usefulness of POS tags needs to be examined as NLP expands to low-resource languages because linguists who provide many annotated resources do not place priority on early identification and tagging of POS. This p... | 2021.acl-long.78 | 10.18653/v1/2021.acl-long.78 | null | null | null |
2021.acl-long.79 | Prosodic segmentation for parsing spoken dialogue | https://aclanthology.org/2021.acl-long.79/ | [
"Elizabeth Nielsen",
"Mark Steedman",
"Sharon Goldwater"
] | Parsing spoken dialogue poses unique difficulties, including disfluencies and unmarked boundaries between sentence-like units. Previous work has shown that prosody can help with parsing disfluent speech (Tran et al. 2018), but has assumed that the input to the parser is already segmented into sentence-like units (SUs),... | 2021.acl-long.79 | 10.18653/v1/2021.acl-long.79 | null | 2105.12667 | title_snapshot |
2021.acl-long.80 | VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation | https://aclanthology.org/2021.acl-long.80/ | [
"Changhan Wang",
"Morgane Riviere",
"Ann Lee",
"Anne Wu",
"Chaitanya Talnikar",
"Daniel Haziza",
"Mary Williamson",
"Juan Pino",
"Emmanuel Dupoux"
] | We introduce VoxPopuli, a large-scale multilingual corpus providing 400K hours of unlabeled speech data in 23 languages. It is the largest open data to date for unsupervised representation learning as well as semi-supervised learning. VoxPopuli also contains 1.8K hours of transcribed speeches in 15 languages and their ... | 2021.acl-long.80 | 10.18653/v1/2021.acl-long.80 | null | 2101.00390 | title_snapshot |
2021.acl-long.81 | Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets | https://aclanthology.org/2021.acl-long.81/ | [
"Su Lin Blodgett",
"Gilsinia Lopez",
"Alexandra Olteanu",
"Robert Sim",
"Hanna Wallach"
] | Auditing NLP systems for computational harms like surfacing stereotypes is an elusive goal. Several recent efforts have focused on benchmark datasets consisting of pairs of contrastive sentences, which are often accompanied by metrics that aggregate an NLP system’s behavior on these pairs into measurements of harms. We... | 2021.acl-long.81 | 10.18653/v1/2021.acl-long.81 | null | null | null |
2021.acl-long.82 | Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network | https://aclanthology.org/2021.acl-long.82/ | [
"Justin Lovelace",
"Denis Newman-Griffis",
"Shikhar Vashishth",
"Jill Fain Lehman",
"Carolyn Rosé"
] | Knowledge Graph (KG) completion research usually focuses on densely connected benchmark datasets that are not representative of real KGs. We curate two KG datasets that include biomedical and encyclopedic knowledge and use an existing commonsense KG dataset to explore KG completion in the more realistic setting where d... | 2021.acl-long.82 | 10.18653/v1/2021.acl-long.82 | null | 2106.06555 | title_snapshot |
2021.acl-long.83 | A DQN-based Approach to Finding Precise Evidences for Fact Verification | https://aclanthology.org/2021.acl-long.83/ | [
"Hai Wan",
"Haicheng Chen",
"Jianfeng Du",
"Weilin Luo",
"Rongzhen Ye"
] | Computing precise evidences, namely minimal sets of sentences that support or refute a given claim, rather than larger evidences is crucial in fact verification (FV), since larger evidences may contain conflicting pieces some of which support the claim while the other refute, thereby misleading FV. Despite being import... | 2021.acl-long.83 | 10.18653/v1/2021.acl-long.83 | null | null | null |
2021.acl-long.84 | The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing | https://aclanthology.org/2021.acl-long.84/ | [
"Ji Xin",
"Raphael Tang",
"Yaoliang Yu",
"Jimmy Lin"
] | In selective prediction, a classifier is allowed to abstain from making predictions on low-confidence examples. Though this setting is interesting and important, selective prediction has rarely been examined in natural language processing (NLP) tasks. To fill this void in the literature, we study in this paper selectiv... | 2021.acl-long.84 | 10.18653/v1/2021.acl-long.84 | null | null | null |
2021.acl-long.85 | Unsupervised Out-of-Domain Detection via Pre-trained Transformers | https://aclanthology.org/2021.acl-long.85/ | [
"Keyang Xu",
"Tongzheng Ren",
"Shikun Zhang",
"Yihao Feng",
"Caiming Xiong"
] | Deployed real-world machine learning applications are often subject to uncontrolled and even potentially malicious inputs. Those out-of-domain inputs can lead to unpredictable outputs and sometimes catastrophic safety issues. Prior studies on out-of-domain detection require in-domain task labels and are limited to supe... | 2021.acl-long.85 | 10.18653/v1/2021.acl-long.85 | null | 2106.00948 | title_snapshot |
2021.acl-long.86 | MATE-KD: Masked Adversarial TExt, a Companion to Knowledge Distillation | https://aclanthology.org/2021.acl-long.86/ | [
"Ahmad Rashid",
"Vasileios Lioutas",
"Mehdi Rezagholizadeh"
] | The advent of large pre-trained language models has given rise to rapid progress in the field of Natural Language Processing (NLP). While the performance of these models on standard benchmarks has scaled with size, compression techniques such as knowledge distillation have been key in making them practical. We present ... | 2021.acl-long.86 | 10.18653/v1/2021.acl-long.86 | null | 2105.05912 | title_snapshot |
2021.acl-long.87 | Selecting Informative Contexts Improves Language Model Fine-tuning | https://aclanthology.org/2021.acl-long.87/ | [
"Richard Antonello",
"Nicole Beckage",
"Javier Turek",
"Alexander Huth"
] | Language model fine-tuning is essential for modern natural language processing, but is computationally expensive and time-consuming. Further, the effectiveness of fine-tuning is limited by the inclusion of training examples that negatively affect performance. Here we present a general fine-tuning method that we call in... | 2021.acl-long.87 | 10.18653/v1/2021.acl-long.87 | null | 2005.00175 | title_judge |
2021.acl-long.88 | Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification | https://aclanthology.org/2021.acl-long.88/ | [
"Cristina Garbacea",
"Mengtian Guo",
"Samuel Carton",
"Qiaozhu Mei"
] | Text simplification reduces the language complexity of professional content for accessibility purposes. End-to-end neural network models have been widely adopted to directly generate the simplified version of input text, usually functioning as a blackbox. We show that text simplification can be decomposed into a compac... | 2021.acl-long.88 | 10.18653/v1/2021.acl-long.88 | null | 2007.15823 | title_snapshot |
2021.acl-long.89 | Multi-Task Retrieval for Knowledge-Intensive Tasks | https://aclanthology.org/2021.acl-long.89/ | [
"Jean Maillard",
"Vladimir Karpukhin",
"Fabio Petroni",
"Wen-tau Yih",
"Barlas Oguz",
"Veselin Stoyanov",
"Gargi Ghosh"
] | Retrieving relevant contexts from a large corpus is a crucial step for tasks such as open-domain question answering and fact checking. Although neural retrieval outperforms traditional methods like tf-idf and BM25, its performance degrades considerably when applied to out-of-domain data. Driven by the question of wheth... | 2021.acl-long.89 | 10.18653/v1/2021.acl-long.89 | null | 2101.00117 | title_snapshot |
2021.acl-long.90 | When Do You Need Billions of Words of Pretraining Data? | https://aclanthology.org/2021.acl-long.90/ | [
"Yian Zhang",
"Alex Warstadt",
"Xiaocheng Li",
"Samuel R. Bowman"
] | NLP is currently dominated by language models like RoBERTa which are pretrained on billions of words. But what exact knowledge or skills do Transformer LMs learn from large-scale pretraining that they cannot learn from less data? To explore this question, we adopt five styles of evaluation: classifier probing, informat... | 2021.acl-long.90 | 10.18653/v1/2021.acl-long.90 | null | 2011.04946 | title_snapshot |
2021.acl-long.91 | Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation | https://aclanthology.org/2021.acl-long.91/ | [
"Elena Voita",
"Rico Sennrich",
"Ivan Titov"
] | In Neural Machine Translation (and, more generally, conditional language modeling), the generation of a target token is influenced by two types of context: the source and the prefix of the target sequence. While many attempts to understand the internal workings of NMT models have been made, none of them explicitly eval... | 2021.acl-long.91 | 10.18653/v1/2021.acl-long.91 | null | 2010.10907 | title_snapshot |
2021.acl-long.92 | Comparing Test Sets with Item Response Theory | https://aclanthology.org/2021.acl-long.92/ | [
"Clara Vania",
"Phu Mon Htut",
"William Huang",
"Dhara Mungra",
"Richard Yuanzhe Pang",
"Jason Phang",
"Haokun Liu",
"Kyunghyun Cho",
"Samuel R. Bowman"
] | Recent years have seen numerous NLP datasets introduced to evaluate the performance of fine-tuned models on natural language understanding tasks. Recent results from large pretrained models, though, show that many of these datasets are largely saturated and unlikely to be able to detect further progress. What kind of d... | 2021.acl-long.92 | 10.18653/v1/2021.acl-long.92 | null | 2106.00840 | title_snapshot |
2021.acl-long.93 | Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning | https://aclanthology.org/2021.acl-long.93/ | [
"Forrest Davis",
"Marten van Schijndel"
] | A growing body of literature has focused on detailing the linguistic knowledge embedded in large, pretrained language models. Existing work has shown that non-linguistic biases in models can drive model behavior away from linguistic generalizations. We hypothesized that competing linguistic processes within a language,... | 2021.acl-long.93 | 10.18653/v1/2021.acl-long.93 | null | 2106.01207 | title_snapshot |
2021.acl-long.94 | More Identifiable yet Equally Performant Transformers for Text Classification | https://aclanthology.org/2021.acl-long.94/ | [
"Rishabh Bhardwaj",
"Navonil Majumder",
"Soujanya Poria",
"Eduard Hovy"
] | Interpretability is an important aspect of the trustworthiness of a model’s predictions. Transformer’s predictions are widely explained by the attention weights, i.e., a probability distribution generated at its self-attention unit (head). Current empirical studies provide shreds of evidence that attention weights are ... | 2021.acl-long.94 | 10.18653/v1/2021.acl-long.94 | null | 2106.01269 | title_snapshot |
2021.acl-long.95 | AugNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation | https://aclanthology.org/2021.acl-long.95/ | [
"Xinnuo Xu",
"Guoyin Wang",
"Young-Bum Kim",
"Sungjin Lee"
] | Natural Language Generation (NLG) is a key component in a task-oriented dialogue system, which converts the structured meaning representation (MR) to the natural language. For large-scale conversational systems, where it is common to have over hundreds of intents and thousands of slots, neither template-based approache... | 2021.acl-long.95 | 10.18653/v1/2021.acl-long.95 | null | 2106.05589 | title_snapshot |
2021.acl-long.96 | Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children’s mindreading ability | https://aclanthology.org/2021.acl-long.96/ | [
"Venelin Kovatchev",
"Phillip Smith",
"Mark Lee",
"Rory Devine"
] | In this paper we implement and compare 7 different data augmentation strategies for the task of automatic scoring of children’s ability to understand others’ thoughts, feelings, and desires (or “mindreading”). We recruit in-domain experts to re-annotate augmented samples and determine to what extent each strategy prese... | 2021.acl-long.96 | 10.18653/v1/2021.acl-long.96 | null | 2106.01635 | title_snapshot |
2021.acl-long.97 | A Dataset and Baselines for Multilingual Reply Suggestion | https://aclanthology.org/2021.acl-long.97/ | [
"Mozhi Zhang",
"Wei Wang",
"Budhaditya Deb",
"Guoqing Zheng",
"Milad Shokouhi",
"Ahmed Hassan Awadallah"
] | Reply suggestion models help users process emails and chats faster. Previous work only studies English reply suggestion. Instead, we present MRS, a multilingual reply suggestion dataset with ten languages. MRS can be used to compare two families of models: 1) retrieval models that select the reply from a fixed set and ... | 2021.acl-long.97 | 10.18653/v1/2021.acl-long.97 | null | 2106.02017 | title_snapshot |
2021.acl-long.98 | What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks? | https://aclanthology.org/2021.acl-long.98/ | [
"Nikita Nangia",
"Saku Sugawara",
"Harsh Trivedi",
"Alex Warstadt",
"Clara Vania",
"Samuel R. Bowman"
] | Crowdsourcing is widely used to create data for common natural language understanding tasks. Despite the importance of these datasets for measuring and refining model understanding of language, there has been little focus on the crowdsourcing methods used for collecting the datasets. In this paper, we compare the effic... | 2021.acl-long.98 | 10.18653/v1/2021.acl-long.98 | null | 2106.00794 | title_snapshot |
2021.acl-long.99 | Align Voting Behavior with Public Statements for Legislator Representation Learning | https://aclanthology.org/2021.acl-long.99/ | [
"Xinyi Mou",
"Zhongyu Wei",
"Lei Chen",
"Shangyi Ning",
"Yancheng He",
"Changjian Jiang",
"Xuanjing Huang"
] | Ideology of legislators is typically estimated by ideal point models from historical records of votes. It represents legislators and legislation as points in a latent space and shows promising results for modeling voting behavior. However, it fails to capture more specific attitudes of legislators toward emerging issue... | 2021.acl-long.99 | 10.18653/v1/2021.acl-long.99 | null | null | null |
2021.acl-long.100 | Measure and Evaluation of Semantic Divergence across Two Languages | https://aclanthology.org/2021.acl-long.100/ | [
"Syrielle Montariol",
"Alexandre Allauzen"
] | Languages are dynamic systems: word usage may change over time, reflecting various societal factors. However, all languages do not evolve identically: the impact of an event, the influence of a trend or thinking, can differ between communities. In this paper, we propose to track these divergences by comparing the evolu... | 2021.acl-long.100 | 10.18653/v1/2021.acl-long.100 | null | null | null |
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