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
values |
|---|---|---|---|---|---|---|---|---|---|
2021.acl-long.401 | Data Augmentation with Adversarial Training for Cross-Lingual NLI | https://aclanthology.org/2021.acl-long.401/ | [
"Xin Dong",
"Yaxin Zhu",
"Zuohui Fu",
"Dongkuan Xu",
"Gerard de Melo"
] | Due to recent pretrained multilingual representation models, it has become feasible to exploit labeled data from one language to train a cross-lingual model that can then be applied to multiple new languages. In practice, however, we still face the problem of scarce labeled data, leading to subpar results. In this pape... | 2021.acl-long.401 | 10.18653/v1/2021.acl-long.401 | null | null | null |
2021.acl-long.402 | Bootstrapped Unsupervised Sentence Representation Learning | https://aclanthology.org/2021.acl-long.402/ | [
"Yan Zhang",
"Ruidan He",
"Zuozhu Liu",
"Lidong Bing",
"Haizhou Li"
] | As high-quality labeled data is scarce, unsupervised sentence representation learning has attracted much attention. In this paper, we propose a new framework with a two-branch Siamese Network which maximizes the similarity between two augmented views of each sentence. Specifically, given one augmented view of the input... | 2021.acl-long.402 | 10.18653/v1/2021.acl-long.402 | null | null | null |
2021.acl-long.403 | Learning Event Graph Knowledge for Abductive Reasoning | https://aclanthology.org/2021.acl-long.403/ | [
"Li Du",
"Xiao Ding",
"Ting Liu",
"Bing Qin"
] | Abductive reasoning aims at inferring the most plausible explanation for observed events, which would play critical roles in various NLP applications, such as reading comprehension and question answering. To facilitate this task, a narrative text based abductive reasoning task \alphaNLI is proposed, together with explo... | 2021.acl-long.403 | 10.18653/v1/2021.acl-long.403 | null | null | null |
2021.acl-long.404 | A Cognitive Regularizer for Language Modeling | https://aclanthology.org/2021.acl-long.404/ | [
"Jason Wei",
"Clara Meister",
"Ryan Cotterell"
] | The uniform information density (UID) hypothesis, which posits that speakers behaving optimally tend to distribute information uniformly across a linguistic signal, has gained traction in psycholinguistics as an explanation for certain syntactic, morphological, and prosodic choices. In this work, we explore whether the... | 2021.acl-long.404 | 10.18653/v1/2021.acl-long.404 | null | 2105.07144 | title_snapshot |
2021.acl-long.405 | Lower Perplexity is Not Always Human-Like | https://aclanthology.org/2021.acl-long.405/ | [
"Tatsuki Kuribayashi",
"Yohei Oseki",
"Takumi Ito",
"Ryo Yoshida",
"Masayuki Asahara",
"Kentaro Inui"
] | In computational psycholinguistics, various language models have been evaluated against human reading behavior (e.g., eye movement) to build human-like computational models. However, most previous efforts have focused almost exclusively on English, despite the recent trend towards linguistic universal within the genera... | 2021.acl-long.405 | 10.18653/v1/2021.acl-long.405 | null | 2106.01229 | title_snapshot |
2021.acl-long.406 | Word Sense Disambiguation: Towards Interactive Context Exploitation from Both Word and Sense Perspectives | https://aclanthology.org/2021.acl-long.406/ | [
"Ming Wang",
"Yinglin Wang"
] | Lately proposed Word Sense Disambiguation (WSD) systems have approached the estimated upper bound of the task on standard evaluation benchmarks. However, these systems typically implement the disambiguation of words in a document almost independently, underutilizing sense and word dependency in context. In this paper, ... | 2021.acl-long.406 | 10.18653/v1/2021.acl-long.406 | null | null | null |
2021.acl-long.407 | A Knowledge-Guided Framework for Frame Identification | https://aclanthology.org/2021.acl-long.407/ | [
"Xuefeng Su",
"Ru Li",
"Xiaoli Li",
"Jeff Z. Pan",
"Hu Zhang",
"Qinghua Chai",
"Xiaoqi Han"
] | Frame Identification (FI) is a fundamental and challenging task in frame semantic parsing. The task aims to find the exact frame evoked by a target word in a given sentence. It is generally regarded as a classification task in existing work, where frames are treated as discrete labels or represented using onehot embedd... | 2021.acl-long.407 | 10.18653/v1/2021.acl-long.407 | null | null | null |
2021.acl-long.408 | Obtaining Better Static Word Embeddings Using Contextual Embedding Models | https://aclanthology.org/2021.acl-long.408/ | [
"Prakhar Gupta",
"Martin Jaggi"
] | The advent of contextual word embeddings — representations of words which incorporate semantic and syntactic information from their context—has led to tremendous improvements on a wide variety of NLP tasks. However, recent contextual models have prohibitively high computational cost in many use-cases and are often hard... | 2021.acl-long.408 | 10.18653/v1/2021.acl-long.408 | null | 2106.04302 | title_snapshot |
2021.acl-long.409 | Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation | https://aclanthology.org/2021.acl-long.409/ | [
"Yingjun Du",
"Nithin Holla",
"Xiantong Zhen",
"Cees Snoek",
"Ekaterina Shutova"
] | A critical challenge faced by supervised word sense disambiguation (WSD) is the lack of large annotated datasets with sufficient coverage of words in their diversity of senses. This inspired recent research on few-shot WSD using meta-learning. While such work has successfully applied meta-learning to learn new word sen... | 2021.acl-long.409 | 10.18653/v1/2021.acl-long.409 | null | 2106.02960 | title_snapshot |
2021.acl-long.410 | LexFit: Lexical Fine-Tuning of Pretrained Language Models | https://aclanthology.org/2021.acl-long.410/ | [
"Ivan Vulić",
"Edoardo Maria Ponti",
"Anna Korhonen",
"Goran Glavaš"
] | Transformer-based language models (LMs) pretrained on large text collections implicitly store a wealth of lexical semantic knowledge, but it is non-trivial to extract that knowledge effectively from their parameters. Inspired by prior work on semantic specialization of static word embedding (WE) models, we show that it... | 2021.acl-long.410 | 10.18653/v1/2021.acl-long.410 | null | null | null |
2021.acl-long.411 | Text-Free Image-to-Speech Synthesis Using Learned Segmental Units | https://aclanthology.org/2021.acl-long.411/ | [
"Wei-Ning Hsu",
"David Harwath",
"Tyler Miller",
"Christopher Song",
"James Glass"
] | In this paper we present the first model for directly synthesizing fluent, natural-sounding spoken audio captions for images that does not require natural language text as an intermediate representation or source of supervision. Instead, we connect the image captioning module and the speech synthesis module with a set ... | 2021.acl-long.411 | 10.18653/v1/2021.acl-long.411 | null | 2012.15454 | title_snapshot |
2021.acl-long.412 | CTFN: Hierarchical Learning for Multimodal Sentiment Analysis Using Coupled-Translation Fusion Network | https://aclanthology.org/2021.acl-long.412/ | [
"Jiajia Tang",
"Kang Li",
"Xuanyu Jin",
"Andrzej Cichocki",
"Qibin Zhao",
"Wanzeng Kong"
] | Multimodal sentiment analysis is the challenging research area that attends to the fusion of multiple heterogeneous modalities. The main challenge is the occurrence of some missing modalities during the multimodal fusion procedure. However, the existing techniques require all modalities as input, thus are sensitive to ... | 2021.acl-long.412 | 10.18653/v1/2021.acl-long.412 | null | null | null |
2021.acl-long.413 | Positional Artefacts Propagate Through Masked Language Model Embeddings | https://aclanthology.org/2021.acl-long.413/ | [
"Ziyang Luo",
"Artur Kulmizev",
"Xiaoxi Mao"
] | In this work, we demonstrate that the contextualized word vectors derived from pretrained masked language model-based encoders share a common, perhaps undesirable pattern across layers. Namely, we find cases of persistent outlier neurons within BERT and RoBERTa’s hidden state vectors that consistently bear the smallest... | 2021.acl-long.413 | 10.18653/v1/2021.acl-long.413 | null | 2011.04393 | title_snapshot |
2021.acl-long.414 | Language Model Evaluation Beyond Perplexity | https://aclanthology.org/2021.acl-long.414/ | [
"Clara Meister",
"Ryan Cotterell"
] | We propose an alternate approach to quantifying how well language models learn natural language: we ask how well they match the statistical tendencies of natural language. To answer this question, we analyze whether text generated from language models exhibits the statistical tendencies present in the human-generated t... | 2021.acl-long.414 | 10.18653/v1/2021.acl-long.414 | null | 2106.00085 | title_snapshot |
2021.acl-long.415 | Learning to Explain: Generating Stable Explanations Fast | https://aclanthology.org/2021.acl-long.415/ | [
"Xuelin Situ",
"Ingrid Zukerman",
"Cecile Paris",
"Sameen Maruf",
"Gholamreza Haffari"
] | The importance of explaining the outcome of a machine learning model, especially a black-box model, is widely acknowledged. Recent approaches explain an outcome by identifying the contributions of input features to this outcome. In environments involving large black-box models or complex inputs, this leads to computati... | 2021.acl-long.415 | 10.18653/v1/2021.acl-long.415 | null | null | null |
2021.acl-long.416 | StereoSet: Measuring stereotypical bias in pretrained language models | https://aclanthology.org/2021.acl-long.416/ | [
"Moin Nadeem",
"Anna Bethke",
"Siva Reddy"
] | A stereotype is an over-generalized belief about a particular group of people, e.g., Asians are good at math or African Americans are athletic. Such beliefs (biases) are known to hurt target groups. Since pretrained language models are trained on large real-world data, they are known to capture stereotypical biases. It... | 2021.acl-long.416 | 10.18653/v1/2021.acl-long.416 | null | 2004.09456 | title_snapshot |
2021.acl-long.417 | Alignment Rationale for Natural Language Inference | https://aclanthology.org/2021.acl-long.417/ | [
"Zhongtao Jiang",
"Yuanzhe Zhang",
"Zhao Yang",
"Jun Zhao",
"Kang Liu"
] | Deep learning models have achieved great success on the task of Natural Language Inference (NLI), though only a few attempts try to explain their behaviors. Existing explanation methods usually pick prominent features such as words or phrases from the input text. However, for NLI, alignments among words or phrases are ... | 2021.acl-long.417 | 10.18653/v1/2021.acl-long.417 | null | null | null |
2021.acl-long.418 | Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators | https://aclanthology.org/2021.acl-long.418/ | [
"Peiyu Liu",
"Ze-Feng Gao",
"Wayne Xin Zhao",
"Zhi-Yuan Xie",
"Zhong-Yi Lu",
"Ji-Rong Wen"
] | This paper presents a novel pre-trained language models (PLM) compression approach based on the matrix product operator (short as MPO) from quantum many-body physics. It can decompose an original matrix into central tensors (containing the core information) and auxiliary tensors (with only a small proportion of paramet... | 2021.acl-long.418 | 10.18653/v1/2021.acl-long.418 | null | 2106.02205 | title_snapshot |
2021.acl-long.419 | On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation | https://aclanthology.org/2021.acl-long.419/ | [
"Wei Zhang",
"Ziming Huang",
"Yada Zhu",
"Guangnan Ye",
"Xiaodong Cui",
"Fan Zhang"
] | In the recent advances of natural language processing, the scale of the state-of-the-art models and datasets is usually extensive, which challenges the application of sample-based explanation methods in many aspects, such as explanation interpretability, efficiency, and faithfulness. In this work, for the first time, w... | 2021.acl-long.419 | 10.18653/v1/2021.acl-long.419 | null | 2106.04753 | title_judge |
2021.acl-long.420 | Syntax-Enhanced Pre-trained Model | https://aclanthology.org/2021.acl-long.420/ | [
"Zenan Xu",
"Daya Guo",
"Duyu Tang",
"Qinliang Su",
"Linjun Shou",
"Ming Gong",
"Wanjun Zhong",
"Xiaojun Quan",
"Daxin Jiang",
"Nan Duan"
] | We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. Such a problem would lead to the n... | 2021.acl-long.420 | 10.18653/v1/2021.acl-long.420 | null | 2012.14116 | title_snapshot |
2021.acl-long.421 | Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation | https://aclanthology.org/2021.acl-long.421/ | [
"Bo Zhang",
"Xiaoming Zhang",
"Yun Liu",
"Lei Cheng",
"Zhoujun Li"
] | Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge of source domain to the unlabeled target domain. Existing methods typically require to learn to adapt the target model by exploiting the source data and sharing the network architecture across domains. However, this pipeline makes the source data risky... | 2021.acl-long.421 | 10.18653/v1/2021.acl-long.421 | null | null | null |
2021.acl-long.422 | Counterfactual Inference for Text Classification Debiasing | https://aclanthology.org/2021.acl-long.422/ | [
"Chen Qian",
"Fuli Feng",
"Lijie Wen",
"Chunping Ma",
"Pengjun Xie"
] | Today’s text classifiers inevitably suffer from unintended dataset biases, especially the document-level label bias and word-level keyword bias, which may hurt models’ generalization. Many previous studies employed data-level manipulations or model-level balancing mechanisms to recover unbiased distributions and thus p... | 2021.acl-long.422 | 10.18653/v1/2021.acl-long.422 | null | null | null |
2021.acl-long.423 | HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation | https://aclanthology.org/2021.acl-long.423/ | [
"Tao Qi",
"Fangzhao Wu",
"Chuhan Wu",
"Peiru Yang",
"Yang Yu",
"Xing Xie",
"Yongfeng Huang"
] | User interest modeling is critical for personalized news recommendation. Existing news recommendation methods usually learn a single user embedding for each user from their previous behaviors to represent their overall interest. However, user interest is usually diverse and multi-grained, which is difficult to be accur... | 2021.acl-long.423 | 10.18653/v1/2021.acl-long.423 | null | 2106.04408 | title_snapshot |
2021.acl-long.424 | PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity | https://aclanthology.org/2021.acl-long.424/ | [
"Tao Qi",
"Fangzhao Wu",
"Chuhan Wu",
"Yongfeng Huang"
] | Personalized news recommendation methods are widely used in online news services. These methods usually recommend news based on the matching between news content and user interest inferred from historical behaviors. However, these methods usually have difficulties in making accurate recommendations to cold-start users,... | 2021.acl-long.424 | 10.18653/v1/2021.acl-long.424 | null | 2106.01300 | title_snapshot |
2021.acl-long.425 | Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims | https://aclanthology.org/2021.acl-long.425/ | [
"Qiang Sheng",
"Juan Cao",
"Xueyao Zhang",
"Xirong Li",
"Lei Zhong"
] | False claims that have been previously fact-checked can still spread on social media. To mitigate their continual spread, detecting previously fact-checked claims is indispensable. Given a claim, existing works focus on providing evidence for detection by reranking candidate fact-checking articles (FC-articles) retriev... | 2021.acl-long.425 | 10.18653/v1/2021.acl-long.425 | null | 2112.10322 | title_snapshot |
2021.acl-long.426 | Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble | https://aclanthology.org/2021.acl-long.426/ | [
"Yi Zhou",
"Xiaoqing Zheng",
"Cho-Jui Hsieh",
"Kai-Wei Chang",
"Xuanjing Huang"
] | Although deep neural networks have achieved prominent performance on many NLP tasks, they are vulnerable to adversarial examples. We propose Dirichlet Neighborhood Ensemble (DNE), a randomized method for training a robust model to defense synonym substitution-based attacks. During training, DNE forms virtual sentences ... | 2021.acl-long.426 | 10.18653/v1/2021.acl-long.426 | null | 2006.11627 | title_judge |
2021.acl-long.427 | Shortformer: Better Language Modeling using Shorter Inputs | https://aclanthology.org/2021.acl-long.427/ | [
"Ofir Press",
"Noah A. Smith",
"Mike Lewis"
] | Increasing the input length has been a driver of progress in language modeling with transformers. We identify conditions where shorter inputs are not harmful, and achieve perplexity and efficiency improvements through two new methods that decrease input length. First, we show that initially training a model on short su... | 2021.acl-long.427 | 10.18653/v1/2021.acl-long.427 | null | 2012.15832 | title_snapshot |
2021.acl-long.428 | BanditMTL: Bandit-based Multi-task Learning for Text Classification | https://aclanthology.org/2021.acl-long.428/ | [
"Yuren Mao",
"Zekai Wang",
"Weiwei Liu",
"Xuemin Lin",
"Wenbin Hu"
] | Task variance regularization, which can be used to improve the generalization of Multi-task Learning (MTL) models, remains unexplored in multi-task text classification. Accordingly, to fill this gap, this paper investigates how the task might be effectively regularized, and consequently proposes a multi-task learning m... | 2021.acl-long.428 | 10.18653/v1/2021.acl-long.428 | null | null | null |
2021.acl-long.429 | Unified Interpretation of Softmax Cross-Entropy and Negative Sampling: With Case Study for Knowledge Graph Embedding | https://aclanthology.org/2021.acl-long.429/ | [
"Hidetaka Kamigaito",
"Katsuhiko Hayashi"
] | In knowledge graph embedding, the theoretical relationship between the softmax cross-entropy and negative sampling loss functions has not been investigated. This makes it difficult to fairly compare the results of the two different loss functions. We attempted to solve this problem by using the Bregman divergence to pr... | 2021.acl-long.429 | 10.18653/v1/2021.acl-long.429 | null | 2106.07250 | title_snapshot |
2021.acl-long.430 | De-Confounded Variational Encoder-Decoder for Logical Table-to-Text Generation | https://aclanthology.org/2021.acl-long.430/ | [
"Wenqing Chen",
"Jidong Tian",
"Yitian Li",
"Hao He",
"Yaohui Jin"
] | Logical table-to-text generation aims to automatically generate fluent and logically faithful text from tables. The task remains challenging where deep learning models often generated linguistically fluent but logically inconsistent text. The underlying reason may be that deep learning models often capture surface-leve... | 2021.acl-long.430 | 10.18653/v1/2021.acl-long.430 | null | null | null |
2021.acl-long.431 | Rethinking Stealthiness of Backdoor Attack against NLP Models | https://aclanthology.org/2021.acl-long.431/ | [
"Wenkai Yang",
"Yankai Lin",
"Peng Li",
"Jie Zhou",
"Xu Sun"
] | Recent researches have shown that large natural language processing (NLP) models are vulnerable to a kind of security threat called the Backdoor Attack. Backdoor attacked models can achieve good performance on clean test sets but perform badly on those input sentences injected with designed trigger words. In this work,... | 2021.acl-long.431 | 10.18653/v1/2021.acl-long.431 | null | null | null |
2021.acl-long.432 | Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition | https://aclanthology.org/2021.acl-long.432/ | [
"Xin Zhang",
"Guangwei Xu",
"Yueheng Sun",
"Meishan Zhang",
"Pengjun Xie"
] | Crowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers. Previous studies focus on reducing the influences from the noises of the crowdsourced annotations for supervised models. We take a different point in this work, ... | 2021.acl-long.432 | 10.18653/v1/2021.acl-long.432 | null | 2105.14980 | title_snapshot |
2021.acl-long.433 | Exploring Distantly-Labeled Rationales in Neural Network Models | https://aclanthology.org/2021.acl-long.433/ | [
"Quzhe Huang",
"Shengqi Zhu",
"Yansong Feng",
"Dongyan Zhao"
] | Recent studies strive to incorporate various human rationales into neural networks to improve model performance, but few pay attention to the quality of the rationales. Most existing methods distribute their models’ focus to distantly-labeled rationale words entirely and equally, while ignoring the potential important ... | 2021.acl-long.433 | 10.18653/v1/2021.acl-long.433 | null | 2106.01809 | title_snapshot |
2021.acl-long.434 | Learning to Perturb Word Embeddings for Out-of-distribution QA | https://aclanthology.org/2021.acl-long.434/ | [
"Seanie Lee",
"Minki Kang",
"Juho Lee",
"Sung Ju Hwang"
] | QA models based on pretrained language models have achieved remarkable performance on various benchmark datasets. However, QA models do not generalize well to unseen data that falls outside the training distribution, due to distributional shifts. Data augmentation (DA) techniques which drop/replace words have shown to ... | 2021.acl-long.434 | 10.18653/v1/2021.acl-long.434 | null | 2105.02692 | title_snapshot |
2021.acl-long.435 | Maria: A Visual Experience Powered Conversational Agent | https://aclanthology.org/2021.acl-long.435/ | [
"Zujie Liang",
"Huang Hu",
"Can Xu",
"Chongyang Tao",
"Xiubo Geng",
"Yining Chen",
"Fan Liang",
"Daxin Jiang"
] | Arguably, the visual perception of conversational agents to the physical world is a key way for them to exhibit the human-like intelligence. Image-grounded conversation is thus proposed to address this challenge. Existing works focus on exploring the multimodal dialog models that ground the conversation on a given imag... | 2021.acl-long.435 | 10.18653/v1/2021.acl-long.435 | null | 2105.13073 | title_snapshot |
2021.acl-long.436 | A Human-machine Collaborative Framework for Evaluating Malevolence in Dialogues | https://aclanthology.org/2021.acl-long.436/ | [
"Yangjun Zhang",
"Pengjie Ren",
"Maarten de Rijke"
] | Conversational dialogue systems (CDSs) are hard to evaluate due to the complexity of natural language. Automatic evaluation of dialogues often shows insufficient correlation with human judgements. Human evaluation is reliable but labor-intensive. We introduce a human-machine collaborative framework, HMCEval, that can g... | 2021.acl-long.436 | 10.18653/v1/2021.acl-long.436 | null | null | null |
2021.acl-long.437 | Generating Relevant and Coherent Dialogue Responses using Self-Separated Conditional Variational AutoEncoders | https://aclanthology.org/2021.acl-long.437/ | [
"Bin Sun",
"Shaoxiong Feng",
"Yiwei Li",
"Jiamou Liu",
"Kan Li"
] | Conditional Variational AutoEncoder (CVAE) effectively increases the diversity and informativeness of responses in open-ended dialogue generation tasks through enriching the context vector with sampled latent variables. However, due to the inherent one-to-many and many-to-one phenomena in human dialogues, the sampled l... | 2021.acl-long.437 | 10.18653/v1/2021.acl-long.437 | null | 2106.03410 | title_snapshot |
2021.acl-long.438 | Learning to Ask Conversational Questions by Optimizing Levenshtein Distance | https://aclanthology.org/2021.acl-long.438/ | [
"Zhongkun Liu",
"Pengjie Ren",
"Zhumin Chen",
"Zhaochun Ren",
"Maarten de Rijke",
"Ming Zhou"
] | Conversational Question Simplification (CQS) aims to simplify self-contained questions into conversational ones by incorporating some conversational characteristics, e.g., anaphora and ellipsis. Existing maximum likelihood estimation based methods often get trapped in easily learned tokens as all tokens are treated equ... | 2021.acl-long.438 | 10.18653/v1/2021.acl-long.438 | null | 2106.15903 | title_snapshot |
2021.acl-long.439 | DVD: A Diagnostic Dataset for Multi-step Reasoning in Video Grounded Dialogue | https://aclanthology.org/2021.acl-long.439/ | [
"Hung Le",
"Chinnadhurai Sankar",
"Seungwhan Moon",
"Ahmad Beirami",
"Alborz Geramifard",
"Satwik Kottur"
] | A video-grounded dialogue system is required to understand both dialogue, which contains semantic dependencies from turn to turn, and video, which contains visual cues of spatial and temporal scene variations. Building such dialogue systems is a challenging problem, involving various reasoning types on both visual and ... | 2021.acl-long.439 | 10.18653/v1/2021.acl-long.439 | null | 2101.00151 | title_snapshot |
2021.acl-long.440 | MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation | https://aclanthology.org/2021.acl-long.440/ | [
"Jingwen Hu",
"Yuchen Liu",
"Jinming Zhao",
"Qin Jin"
] | Emotion recognition in conversation (ERC) is a crucial component in affective dialogue systems, which helps the system understand users’ emotions and generate empathetic responses. However, most works focus on modeling speaker and contextual information primarily on the textual modality or simply leveraging multimodal ... | 2021.acl-long.440 | 10.18653/v1/2021.acl-long.440 | null | 2107.06779 | title_snapshot |
2021.acl-long.441 | DynaEval: Unifying Turn and Dialogue Level Evaluation | https://aclanthology.org/2021.acl-long.441/ | [
"Chen Zhang",
"Yiming Chen",
"Luis Fernando D’Haro",
"Yan Zhang",
"Thomas Friedrichs",
"Grandee Lee",
"Haizhou Li"
] | A dialogue is essentially a multi-turn interaction among interlocutors. Effective evaluation metrics should reflect the dynamics of such interaction. Existing automatic metrics are focused very much on the turn-level quality, while ignoring such dynamics. To this end, we propose DynaEval, a unified automatic evaluation... | 2021.acl-long.441 | 10.18653/v1/2021.acl-long.441 | null | 2106.01112 | title_snapshot |
2021.acl-long.442 | CoSQA: 20,000+ Web Queries for Code Search and Question Answering | https://aclanthology.org/2021.acl-long.442/ | [
"Junjie Huang",
"Duyu Tang",
"Linjun Shou",
"Ming Gong",
"Ke Xu",
"Daxin Jiang",
"Ming Zhou",
"Nan Duan"
] | Finding codes given natural language query is beneficial to the productivity of software developers. Future progress towards better semantic matching between query and code requires richer supervised training resources. To remedy this, we introduce CoSQA dataset. It includes 20,604 labels for pairs of natural language ... | 2021.acl-long.442 | 10.18653/v1/2021.acl-long.442 | null | 2105.13239 | title_snapshot |
2021.acl-long.443 | Rewriter-Evaluator Architecture for Neural Machine Translation | https://aclanthology.org/2021.acl-long.443/ | [
"Yangming Li",
"Kaisheng Yao"
] | A few approaches have been developed to improve neural machine translation (NMT) models with multiple passes of decoding. However, their performance gains are limited because of lacking proper policies to terminate the multi-pass process. To address this issue, we introduce a novel architecture of Rewriter-Evaluator. T... | 2021.acl-long.443 | 10.18653/v1/2021.acl-long.443 | null | 2012.05414 | title_snapshot |
2021.acl-long.444 | Modeling Bilingual Conversational Characteristics for Neural Chat Translation | https://aclanthology.org/2021.acl-long.444/ | [
"Yunlong Liang",
"Fandong Meng",
"Yufeng Chen",
"Jinan Xu",
"Jie Zhou"
] | Neural chat translation aims to translate bilingual conversational text, which has a broad application in international exchanges and cooperation. Despite the impressive performance of sentence-level and context-aware Neural Machine Translation (NMT), there still remain challenges to translate bilingual conversational ... | 2021.acl-long.444 | 10.18653/v1/2021.acl-long.444 | null | 2107.11164 | title_snapshot |
2021.acl-long.445 | Importance-based Neuron Allocation for Multilingual Neural Machine Translation | https://aclanthology.org/2021.acl-long.445/ | [
"Wanying Xie",
"Yang Feng",
"Shuhao Gu",
"Dong Yu"
] | Multilingual neural machine translation with a single model has drawn much attention due to its capability to deal with multiple languages. However, the current multilingual translation paradigm often makes the model tend to preserve the general knowledge, but ignore the language-specific knowledge. Some previous works... | 2021.acl-long.445 | 10.18653/v1/2021.acl-long.445 | null | 2107.06569 | title_snapshot |
2021.acl-long.446 | Transfer Learning for Sequence Generation: from Single-source to Multi-source | https://aclanthology.org/2021.acl-long.446/ | [
"Xuancheng Huang",
"Jingfang Xu",
"Maosong Sun",
"Yang Liu"
] | Multi-source sequence generation (MSG) is an important kind of sequence generation tasks that takes multiple sources, including automatic post-editing, multi-source translation, multi-document summarization, etc. As MSG tasks suffer from the data scarcity problem and recent pretrained models have been proven to be effe... | 2021.acl-long.446 | 10.18653/v1/2021.acl-long.446 | null | 2105.14809 | title_snapshot |
2021.acl-long.447 | A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters | https://aclanthology.org/2021.acl-long.447/ | [
"Mengjie Zhao",
"Yi Zhu",
"Ehsan Shareghi",
"Ivan Vulić",
"Roi Reichart",
"Anna Korhonen",
"Hinrich Schütze"
] | Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT. Despite its growing popularity, little to no attention has been paid to standardizing and analyzing the design of few-shot experiments. In this work, we highlight a fundamental risk pos... | 2021.acl-long.447 | 10.18653/v1/2021.acl-long.447 | null | 2012.15682 | title_snapshot |
2021.acl-long.448 | Coreference Reasoning in Machine Reading Comprehension | https://aclanthology.org/2021.acl-long.448/ | [
"Mingzhu Wu",
"Nafise Sadat Moosavi",
"Dan Roth",
"Iryna Gurevych"
] | Coreference resolution is essential for natural language understanding and has been long studied in NLP. In recent years, as the format of Question Answering (QA) became a standard for machine reading comprehension (MRC), there have been data collection efforts, e.g., Dasigi et al. (2019), that attempt to evaluate the ... | 2021.acl-long.448 | 10.18653/v1/2021.acl-long.448 | null | 2012.15573 | title_snapshot |
2021.acl-long.449 | Adapting Unsupervised Syntactic Parsing Methodology for Discourse Dependency Parsing | https://aclanthology.org/2021.acl-long.449/ | [
"Liwen Zhang",
"Ge Wang",
"Wenjuan Han",
"Kewei Tu"
] | One of the main bottlenecks in developing discourse dependency parsers is the lack of annotated training data. A potential solution is to utilize abundant unlabeled data by using unsupervised techniques, but there is so far little research in unsupervised discourse dependency parsing. Fortunately, unsupervised syntacti... | 2021.acl-long.449 | 10.18653/v1/2021.acl-long.449 | null | null | null |
2021.acl-long.450 | A Conditional Splitting Framework for Efficient Constituency Parsing | https://aclanthology.org/2021.acl-long.450/ | [
"Thanh-Tung Nguyen",
"Xuan-Phi Nguyen",
"Shafiq Joty",
"Xiaoli Li"
] | We introduce a generic seq2seq parsing framework that casts constituency parsing problems (syntactic and discourse parsing) into a series of conditional splitting decisions. Our parsing model estimates the conditional probability distribution of possible splitting points in a given text span and supports efficient top-... | 2021.acl-long.450 | 10.18653/v1/2021.acl-long.450 | null | 2106.15760 | title_snapshot |
2021.acl-long.451 | A Unified Generative Framework for Various NER Subtasks | https://aclanthology.org/2021.acl-long.451/ | [
"Hang Yan",
"Tao Gui",
"Junqi Dai",
"Qipeng Guo",
"Zheng Zhang",
"Xipeng Qiu"
] | Named Entity Recognition (NER) is the task of identifying spans that represent entities in sentences. Whether the entity spans are nested or discontinuous, the NER task can be categorized into the flat NER, nested NER, and discontinuous NER subtasks. These subtasks have been mainly solved by the token-level sequence la... | 2021.acl-long.451 | 10.18653/v1/2021.acl-long.451 | null | 2106.01223 | title_snapshot |
2021.acl-long.452 | An In-depth Study on Internal Structure of Chinese Words | https://aclanthology.org/2021.acl-long.452/ | [
"Chen Gong",
"Saihao Huang",
"Houquan Zhou",
"Zhenghua Li",
"Min Zhang",
"Zhefeng Wang",
"Baoxing Huai",
"Nicholas Jing Yuan"
] | Unlike English letters, Chinese characters have rich and specific meanings. Usually, the meaning of a word can be derived from its constituent characters in some way. Several previous works on syntactic parsing propose to annotate shallow word-internal structures for better utilizing character-level information. This w... | 2021.acl-long.452 | 10.18653/v1/2021.acl-long.452 | null | 2106.00334 | title_snapshot |
2021.acl-long.453 | MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER | https://aclanthology.org/2021.acl-long.453/ | [
"Linlin Liu",
"Bosheng Ding",
"Lidong Bing",
"Shafiq Joty",
"Luo Si",
"Chunyan Miao"
] | Named Entity Recognition (NER) for low-resource languages is a both practical and challenging research problem. This paper addresses zero-shot transfer for cross-lingual NER, especially when the amount of source-language training data is also limited. The paper first proposes a simple but effective labeled sequence tra... | 2021.acl-long.453 | 10.18653/v1/2021.acl-long.453 | null | null | null |
2021.acl-long.454 | Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter | https://aclanthology.org/2021.acl-long.454/ | [
"Wei Liu",
"Xiyan Fu",
"Yue Zhang",
"Wenming Xiao"
] | Lexicon information and pre-trained models, such as BERT, have been combined to explore Chinese sequence labeling tasks due to their respective strengths. However, existing methods solely fuse lexicon features via a shallow and random initialized sequence layer and do not integrate them into the bottom layers of BERT. ... | 2021.acl-long.454 | 10.18653/v1/2021.acl-long.454 | null | 2105.07148 | title_snapshot |
2021.acl-long.455 | Math Word Problem Solving with Explicit Numerical Values | https://aclanthology.org/2021.acl-long.455/ | [
"Qinzhuo Wu",
"Qi Zhang",
"Zhongyu Wei",
"Xuanjing Huang"
] | In recent years, math word problem solving has received considerable attention and achieved promising results, but previous methods rarely take numerical values into consideration. Most methods treat the numerical values in the problems as number symbols, and ignore the prominent role of the numerical values in solving... | 2021.acl-long.455 | 10.18653/v1/2021.acl-long.455 | null | null | null |
2021.acl-long.456 | Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks | https://aclanthology.org/2021.acl-long.456/ | [
"Jinghui Qin",
"Xiaodan Liang",
"Yining Hong",
"Jianheng Tang",
"Liang Lin"
] | Previous math word problem solvers following the encoder-decoder paradigm fail to explicitly incorporate essential math symbolic constraints, leading to unexplainable and unreasonable predictions. Herein, we propose Neural-Symbolic Solver (NS-Solver) to explicitly and seamlessly incorporate different levels of symbolic... | 2021.acl-long.456 | 10.18653/v1/2021.acl-long.456 | null | 2107.01431 | title_snapshot |
2021.acl-long.457 | SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining | https://aclanthology.org/2021.acl-long.457/ | [
"Taolin Zhang",
"Zerui Cai",
"Chengyu Wang",
"Minghui Qiu",
"Bite Yang",
"Xiaofeng He"
] | Recently, the performance of Pre-trained Language Models (PLMs) has been significantly improved by injecting knowledge facts to enhance their abilities of language understanding. For medical domains, the background knowledge sources are especially useful, due to the massive medical terms and their complicated relations... | 2021.acl-long.457 | 10.18653/v1/2021.acl-long.457 | null | 2108.08983 | title_snapshot |
2021.acl-long.458 | What is Your Article Based On? Inferring Fine-grained Provenance | https://aclanthology.org/2021.acl-long.458/ | [
"Yi Zhang",
"Zachary Ives",
"Dan Roth"
] | When evaluating an article and the claims it makes, a critical reader must be able to assess where the information presented comes from, and whether the various claims are mutually consistent and support the conclusion. This motivates the study of claim provenance, which seeks to trace and explain the origins of claims... | 2021.acl-long.458 | 10.18653/v1/2021.acl-long.458 | null | null | null |
2021.acl-long.459 | Cross-modal Memory Networks for Radiology Report Generation | https://aclanthology.org/2021.acl-long.459/ | [
"Zhihong Chen",
"Yaling Shen",
"Yan Song",
"Xiang Wan"
] | Medical imaging plays a significant role in clinical practice of medical diagnosis, where the text reports of the images are essential in understanding them and facilitating later treatments. By generating the reports automatically, it is beneficial to help lighten the burden of radiologists and significantly promote c... | 2021.acl-long.459 | 10.18653/v1/2021.acl-long.459 | null | 2204.13258 | title_snapshot |
2021.acl-long.460 | Controversy and Conformity: from Generalized to Personalized Aggressiveness Detection | https://aclanthology.org/2021.acl-long.460/ | [
"Kamil Kanclerz",
"Alicja Figas",
"Marcin Gruza",
"Tomasz Kajdanowicz",
"Jan Kocon",
"Daria Puchalska",
"Przemyslaw Kazienko"
] | There is content such as hate speech, offensive, toxic or aggressive documents, which are perceived differently by their consumers. They are commonly identified using classifiers solely based on textual content that generalize pre-agreed meanings of difficult problems. Such models provide the same results for each user... | 2021.acl-long.460 | 10.18653/v1/2021.acl-long.460 | null | null | null |
2021.acl-long.461 | Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews | https://aclanthology.org/2021.acl-long.461/ | [
"Junhao Liu",
"Zhen Hai",
"Min Yang",
"Lidong Bing"
] | As more and more product reviews are posted in both text and images, Multimodal Review Analysis (MRA) becomes an attractive research topic. Among the existing review analysis tasks, helpfulness prediction on review text has become predominant due to its importance for e-commerce platforms and online shops, i.e. helping... | 2021.acl-long.461 | 10.18653/v1/2021.acl-long.461 | null | null | null |
2021.acl-long.462 | Instantaneous Grammatical Error Correction with Shallow Aggressive Decoding | https://aclanthology.org/2021.acl-long.462/ | [
"Xin Sun",
"Tao Ge",
"Furu Wei",
"Houfeng Wang"
] | In this paper, we propose Shallow Aggressive Decoding (SAD) to improve the online inference efficiency of the Transformer for instantaneous Grammatical Error Correction (GEC). SAD optimizes the online inference efficiency for GEC by two innovations: 1) it aggressively decodes as many tokens as possible in parallel inst... | 2021.acl-long.462 | 10.18653/v1/2021.acl-long.462 | null | 2106.04970 | title_snapshot |
2021.acl-long.463 | Automatic ICD Coding via Interactive Shared Representation Networks with Self-distillation Mechanism | https://aclanthology.org/2021.acl-long.463/ | [
"Tong Zhou",
"Pengfei Cao",
"Yubo Chen",
"Kang Liu",
"Jun Zhao",
"Kun Niu",
"Weifeng Chong",
"Shengping Liu"
] | The ICD coding task aims at assigning codes of the International Classification of Diseases in clinical notes. Since manual coding is very laborious and prone to errors, many methods have been proposed for the automatic ICD coding task. However, existing works either ignore the long-tail of code frequency or the noisy ... | 2021.acl-long.463 | 10.18653/v1/2021.acl-long.463 | null | null | null |
2021.acl-long.464 | PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check | https://aclanthology.org/2021.acl-long.464/ | [
"Li Huang",
"Junjie Li",
"Weiwei Jiang",
"Zhiyu Zhang",
"Minchuan Chen",
"Shaojun Wang",
"Jing Xiao"
] | Chinese Spelling Check (CSC) is a challenging task due to the complex characteristics of Chinese characters. Statistics reveal that most Chinese spelling errors belong to phonological or visual errors. However, previous methods rarely utilize phonological and morphological knowledge of Chinese characters or heavily rel... | 2021.acl-long.464 | 10.18653/v1/2021.acl-long.464 | null | null | null |
2021.acl-long.465 | Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting | https://aclanthology.org/2021.acl-long.465/ | [
"Yi Cheng",
"Siyao Li",
"Bang Liu",
"Ruihui Zhao",
"Sujian Li",
"Chenghua Lin",
"Yefeng Zheng"
] | This paper explores the task of Difficulty-Controllable Question Generation (DCQG), which aims at generating questions with required difficulty levels. Previous research on this task mainly defines the difficulty of a question as whether it can be correctly answered by a Question Answering (QA) system, lacking interpre... | 2021.acl-long.465 | 10.18653/v1/2021.acl-long.465 | null | 2105.11698 | title_snapshot |
2021.acl-long.466 | Improving Encoder by Auxiliary Supervision Tasks for Table-to-Text Generation | https://aclanthology.org/2021.acl-long.466/ | [
"Liang Li",
"Can Ma",
"Yinliang Yue",
"Dayong Hu"
] | Table-to-text generation aims at automatically generating natural text to help people conveniently obtain salient information in tables. Although neural models for table-to-text have achieved remarkable progress, some problems are still overlooked. Previous methods cannot deduce the factual results from the entity’s (p... | 2021.acl-long.466 | 10.18653/v1/2021.acl-long.466 | null | null | null |
2021.acl-long.467 | POS-Constrained Parallel Decoding for Non-autoregressive Generation | https://aclanthology.org/2021.acl-long.467/ | [
"Kexin Yang",
"Wenqiang Lei",
"Dayiheng Liu",
"Weizhen Qi",
"Jiancheng Lv"
] | The multimodality problem has become a major challenge of existing non-autoregressive generation (NAG) systems. A common solution often resorts to sequence-level knowledge distillation by rebuilding the training dataset through autoregressive generation (hereinafter known as “teacher AG”). The success of such methods m... | 2021.acl-long.467 | 10.18653/v1/2021.acl-long.467 | null | null | null |
2021.acl-long.468 | Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation | https://aclanthology.org/2021.acl-long.468/ | [
"Xin Liu",
"Baosong Yang",
"Dayiheng Liu",
"Haibo Zhang",
"Weihua Luo",
"Min Zhang",
"Haiying Zhang",
"Jinsong Su"
] | A well-known limitation in pretrain-finetune paradigm lies in its inflexibility caused by the one-size-fits-all vocabulary. This potentially weakens the effect when applying pretrained models into natural language generation (NLG) tasks, especially for the subword distributions between upstream and downstream tasks wit... | 2021.acl-long.468 | 10.18653/v1/2021.acl-long.468 | null | 2106.06125 | title_snapshot |
2021.acl-long.469 | TGEA: An Error-Annotated Dataset and Benchmark Tasks for TextGeneration from Pretrained Language Models | https://aclanthology.org/2021.acl-long.469/ | [
"Jie He",
"Bo Peng",
"Yi Liao",
"Qun Liu",
"Deyi Xiong"
] | In order to deeply understand the capability of pretrained language models in text generation and conduct a diagnostic evaluation, we propose TGEA, an error-annotated dataset with multiple benchmark tasks for text generation from pretrained language models (PLMs). We use carefully selected prompt words to guide GPT-2 t... | 2021.acl-long.469 | 10.18653/v1/2021.acl-long.469 | null | null | null |
2021.acl-long.470 | Long-Span Summarization via Local Attention and Content Selection | https://aclanthology.org/2021.acl-long.470/ | [
"Potsawee Manakul",
"Mark Gales"
] | Transformer-based models have achieved state-of-the-art results in a wide range of natural language processing (NLP) tasks including document summarization. Typically these systems are trained by fine-tuning a large pre-trained model to the target task. One issue with these transformer-based models is that they do not ... | 2021.acl-long.470 | 10.18653/v1/2021.acl-long.470 | null | 2105.03801 | title_snapshot |
2021.acl-long.471 | RepSum: Unsupervised Dialogue Summarization based on Replacement Strategy | https://aclanthology.org/2021.acl-long.471/ | [
"Xiyan Fu",
"Yating Zhang",
"Tianyi Wang",
"Xiaozhong Liu",
"Changlong Sun",
"Zhenglu Yang"
] | In the field of dialogue summarization, due to the lack of training data, it is often difficult for supervised summary generation methods to learn vital information from dialogue context with limited data. Several attempts on unsupervised summarization for text by leveraging semantic information solely or auto-encoder ... | 2021.acl-long.471 | 10.18653/v1/2021.acl-long.471 | null | null | null |
2021.acl-long.472 | BASS: Boosting Abstractive Summarization with Unified Semantic Graph | https://aclanthology.org/2021.acl-long.472/ | [
"Wenhao Wu",
"Wei Li",
"Xinyan Xiao",
"Jiachen Liu",
"Ziqiang Cao",
"Sujian Li",
"Hua Wu",
"Haifeng Wang"
] | Abstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text. In this paper, we present BASS, a novel framework for Boosting Abstractive Summarization based on a unified Semantic graph, which aggregate... | 2021.acl-long.472 | 10.18653/v1/2021.acl-long.472 | null | 2105.12041 | title_snapshot |
2021.acl-long.473 | Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation | https://aclanthology.org/2021.acl-long.473/ | [
"Xiuying Chen",
"Hind Alamro",
"Mingzhe Li",
"Shen Gao",
"Xiangliang Zhang",
"Dongyan Zhao",
"Rui Yan"
] | Given a set of related publications, related work section generation aims to provide researchers with an overview of the specific research area by summarizing these works and introducing them in a logical order. Most of existing related work generation models follow the inflexible extractive style, which directly extra... | 2021.acl-long.473 | 10.18653/v1/2021.acl-long.473 | null | null | null |
2021.acl-long.474 | Focus Attention: Promoting Faithfulness and Diversity in Summarization | https://aclanthology.org/2021.acl-long.474/ | [
"Rahul Aralikatte",
"Shashi Narayan",
"Joshua Maynez",
"Sascha Rothe",
"Ryan McDonald"
] | Professional summaries are written with document-level information, such as the theme of the document, in mind. This is in contrast with most seq2seq decoders which simultaneously learn to focus on salient content, while deciding what to generate, at each decoding step. With the motivation to narrow this gap, we introd... | 2021.acl-long.474 | 10.18653/v1/2021.acl-long.474 | null | 2105.11921 | title_snapshot |
2021.acl-long.475 | Generating Query Focused Summaries from Query-Free Resources | https://aclanthology.org/2021.acl-long.475/ | [
"Yumo Xu",
"Mirella Lapata"
] | The availability of large-scale datasets has driven the development of neural models that create generic summaries from single or multiple documents. In this work we consider query focused summarization (QFS), a task for which training data in the form of queries, documents, and summaries is not readily available. We p... | 2021.acl-long.475 | 10.18653/v1/2021.acl-long.475 | null | 2012.14774 | title_snapshot |
2021.acl-long.476 | Robustifying Multi-hop QA through Pseudo-Evidentiality Training | https://aclanthology.org/2021.acl-long.476/ | [
"Kyungjae Lee",
"Seung-won Hwang",
"Sang-eun Han",
"Dohyeon Lee"
] | This paper studies the bias problem of multi-hop question answering models, of answering correctly without correct reasoning. One way to robustify these models is by supervising to not only answer right, but also with right reasoning chains. An existing direction is to annotate reasoning chains to train models, requiri... | 2021.acl-long.476 | 10.18653/v1/2021.acl-long.476 | null | 2107.03242 | title_snapshot |
2021.acl-long.477 | xMoCo: Cross Momentum Contrastive Learning for Open-Domain Question Answering | https://aclanthology.org/2021.acl-long.477/ | [
"Nan Yang",
"Furu Wei",
"Binxing Jiao",
"Daxing Jiang",
"Linjun Yang"
] | Dense passage retrieval has been shown to be an effective approach for information retrieval tasks such as open domain question answering. Under this paradigm, a dual-encoder model is learned to encode questions and passages separately into vector representations, and all the passage vectors are then pre-computed and i... | 2021.acl-long.477 | 10.18653/v1/2021.acl-long.477 | null | null | null |
2021.acl-long.478 | Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering | https://aclanthology.org/2021.acl-long.478/ | [
"Gangwoo Kim",
"Hyunjae Kim",
"Jungsoo Park",
"Jaewoo Kang"
] | One of the main challenges in conversational question answering (CQA) is to resolve the conversational dependency, such as anaphora and ellipsis. However, existing approaches do not explicitly train QA models on how to resolve the dependency, and thus these models are limited in understanding human dialogues. In this p... | 2021.acl-long.478 | 10.18653/v1/2021.acl-long.478 | null | 2106.11575 | title_snapshot |
2021.acl-long.479 | PhotoChat: A Human-Human Dialogue Dataset With Photo Sharing Behavior For Joint Image-Text Modeling | https://aclanthology.org/2021.acl-long.479/ | [
"Xiaoxue Zang",
"Lijuan Liu",
"Maria Wang",
"Yang Song",
"Hao Zhang",
"Jindong Chen"
] | We present a new human-human dialogue dataset - PhotoChat, the first dataset that casts light on the photo sharing behavior in online messaging. PhotoChat contains 12k dialogues, each of which is paired with a user photo that is shared during the conversation. Based on this dataset, we propose two tasks to facilitate r... | 2021.acl-long.479 | 10.18653/v1/2021.acl-long.479 | null | 2108.01453 | title_snapshot |
2021.acl-long.480 | Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation | https://aclanthology.org/2021.acl-long.480/ | [
"Zhiyong Wu",
"Lingpeng Kong",
"Wei Bi",
"Xiang Li",
"Ben Kao"
] | A neural multimodal machine translation (MMT) system is one that aims to perform better translation by extending conventional text-only translation models with multimodal information. Many recent studies report improvements when equipping their models with the multimodal module, despite the controversy of whether such ... | 2021.acl-long.480 | 10.18653/v1/2021.acl-long.480 | null | 2105.14462 | title_snapshot |
2021.acl-long.481 | Attend What You Need: Motion-Appearance Synergistic Networks for Video Question Answering | https://aclanthology.org/2021.acl-long.481/ | [
"Ahjeong Seo",
"Gi-Cheon Kang",
"Joonhan Park",
"Byoung-Tak Zhang"
] | Video Question Answering is a task which requires an AI agent to answer questions grounded in video. This task entails three key challenges: (1) understand the intention of various questions, (2) capturing various elements of the input video (e.g., object, action, causality), and (3) cross-modal grounding between langu... | 2021.acl-long.481 | 10.18653/v1/2021.acl-long.481 | null | 2106.10446 | title_snapshot |
2021.acl-long.482 | BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition | https://aclanthology.org/2021.acl-long.482/ | [
"Yinghao Li",
"Pranav Shetty",
"Lucas Liu",
"Chao Zhang",
"Le Song"
] | We study the problem of learning a named entity recognition (NER) tagger using noisy labels from multiple weak supervision sources. Though cheap to obtain, the labels from weak supervision sources are often incomplete, inaccurate, and contradictory, making it difficult to learn an accurate NER model. To address this ch... | 2021.acl-long.482 | 10.18653/v1/2021.acl-long.482 | null | 2105.12848 | title_snapshot |
2021.acl-long.483 | CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction | https://aclanthology.org/2021.acl-long.483/ | [
"Tao Chen",
"Haizhou Shi",
"Siliang Tang",
"Zhigang Chen",
"Fei Wu",
"Yueting Zhuang"
] | The journey of reducing noise from distant supervision (DS) generated training data has been started since the DS was first introduced into the relation extraction (RE) task. For the past decade, researchers apply the multi-instance learning (MIL) framework to find the most reliable feature from a bag of sentences. Alt... | 2021.acl-long.483 | 10.18653/v1/2021.acl-long.483 | null | 2106.10855 | title_snapshot |
2021.acl-long.484 | SENT: Sentence-level Distant Relation Extraction via Negative Training | https://aclanthology.org/2021.acl-long.484/ | [
"Ruotian Ma",
"Tao Gui",
"Linyang Li",
"Qi Zhang",
"Xuanjing Huang",
"Yaqian Zhou"
] | Distant supervision for relation extraction provides uniform bag labels for each sentence inside the bag, while accurate sentence labels are important for downstream applications that need the exact relation type. Directly using bag labels for sentence-level training will introduce much noise, thus severely degrading p... | 2021.acl-long.484 | 10.18653/v1/2021.acl-long.484 | null | 2106.11566 | title_snapshot |
2021.acl-long.485 | An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization | https://aclanthology.org/2021.acl-long.485/ | [
"Baohang Zhou",
"Xiangrui Cai",
"Ying Zhang",
"Xiaojie Yuan"
] | Medical named entity recognition (NER) and normalization (NEN) are fundamental for constructing knowledge graphs and building QA systems. Existing implementations for medical NER and NEN are suffered from the error propagation between the two tasks. The mispredicted mentions from NER will directly influence the results... | 2021.acl-long.485 | 10.18653/v1/2021.acl-long.485 | null | null | null |
2021.acl-long.486 | PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction | https://aclanthology.org/2021.acl-long.486/ | [
"Hengyi Zheng",
"Rui Wen",
"Xi Chen",
"Yifan Yang",
"Yunyan Zhang",
"Ziheng Zhang",
"Ningyu Zhang",
"Bin Qin",
"Xu Ming",
"Yefeng Zheng"
] | Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction. Recent methods achieve considerable performance but still suffer from some inherent limitations, such as redundancy of relation prediction, poor generalization of span-based extraction and inefficiency. In th... | 2021.acl-long.486 | 10.18653/v1/2021.acl-long.486 | null | 2106.09895 | title_snapshot |
2021.acl-long.487 | Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition | https://aclanthology.org/2021.acl-long.487/ | [
"Meihan Tong",
"Shuai Wang",
"Bin Xu",
"Yixin Cao",
"Minghui Liu",
"Lei Hou",
"Juanzi Li"
] | Few-shot Named Entity Recognition (NER) exploits only a handful of annotations to iden- tify and classify named entity mentions. Pro- totypical network shows superior performance on few-shot NER. However, existing prototyp- ical methods fail to differentiate rich seman- tics in other-class words, which will aggravate o... | 2021.acl-long.487 | 10.18653/v1/2021.acl-long.487 | null | 2106.15167 | title_snapshot |
2021.acl-long.488 | Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference | https://aclanthology.org/2021.acl-long.488/ | [
"Tuan Lai",
"Heng Ji",
"ChengXiang Zhai",
"Quan Hung Tran"
] | Compared to the general news domain, information extraction (IE) from biomedical text requires much broader domain knowledge. However, many previous IE methods do not utilize any external knowledge during inference. Due to the exponential growth of biomedical publications, models that do not go beyond their fixed set o... | 2021.acl-long.488 | 10.18653/v1/2021.acl-long.488 | null | 2105.13456 | title_snapshot |
2021.acl-long.489 | Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation | https://aclanthology.org/2021.acl-long.489/ | [
"Zixuan Zhang",
"Nikolaus Parulian",
"Heng Ji",
"Ahmed Elsayed",
"Skatje Myers",
"Martha Palmer"
] | Biomedical Information Extraction from scientific literature presents two unique and non-trivial challenges. First, compared with general natural language texts, sentences from scientific papers usually possess wider contexts between knowledge elements. Moreover, comprehending the fine-grained scientific entities and e... | 2021.acl-long.489 | 10.18653/v1/2021.acl-long.489 | null | null | null |
2021.acl-long.490 | Unleash GPT-2 Power for Event Detection | https://aclanthology.org/2021.acl-long.490/ | [
"Amir Pouran Ben Veyseh",
"Viet Lai",
"Franck Dernoncourt",
"Thien Huu Nguyen"
] | Event Detection (ED) aims to recognize mentions of events (i.e., event triggers) and their types in text. Recently, several ED datasets in various domains have been proposed. However, the major limitation of these resources is the lack of enough training data for individual event types which hinders the efficient train... | 2021.acl-long.490 | 10.18653/v1/2021.acl-long.490 | null | null | null |
2021.acl-long.491 | CLEVE: Contrastive Pre-training for Event Extraction | https://aclanthology.org/2021.acl-long.491/ | [
"Ziqi Wang",
"Xiaozhi Wang",
"Xu Han",
"Yankai Lin",
"Lei Hou",
"Zhiyuan Liu",
"Peng Li",
"Juanzi Li",
"Jie Zhou"
] | Event extraction (EE) has considerably benefited from pre-trained language models (PLMs) by fine-tuning. However, existing pre-training methods have not involved modeling event characteristics, resulting in the developed EE models cannot take full advantage of large-scale unsupervised data. To this end, we propose CLEV... | 2021.acl-long.491 | 10.18653/v1/2021.acl-long.491 | null | 2105.14485 | title_snapshot |
2021.acl-long.492 | Document-level Event Extraction via Parallel Prediction Networks | https://aclanthology.org/2021.acl-long.492/ | [
"Hang Yang",
"Dianbo Sui",
"Yubo Chen",
"Kang Liu",
"Jun Zhao",
"Taifeng Wang"
] | Document-level event extraction (DEE) is indispensable when events are described throughout a document. We argue that sentence-level extractors are ill-suited to the DEE task where event arguments always scatter across sentences and multiple events may co-exist in a document. It is a challenging task because it require... | 2021.acl-long.492 | 10.18653/v1/2021.acl-long.492 | null | null | null |
2021.acl-long.493 | StructuralLM: Structural Pre-training for Form Understanding | https://aclanthology.org/2021.acl-long.493/ | [
"Chenliang Li",
"Bin Bi",
"Ming Yan",
"Wei Wang",
"Songfang Huang",
"Fei Huang",
"Luo Si"
] | Large pre-trained language models achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, they almost exclusively focus on text-only representation, while neglecting cell-level layout information that is important for form image understanding. In this paper, we propose a new pre-training appr... | 2021.acl-long.493 | 10.18653/v1/2021.acl-long.493 | null | 2105.11210 | title_snapshot |
2021.acl-long.494 | Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis | https://aclanthology.org/2021.acl-long.494/ | [
"Ruifan Li",
"Hao Chen",
"Fangxiang Feng",
"Zhanyu Ma",
"Xiaojie Wang",
"Eduard Hovy"
] | Aspect-based sentiment analysis is a fine-grained sentiment classification task. Recently, graph neural networks over dependency trees have been explored to explicitly model connections between aspects and opinion words. However, the improvement is limited due to the inaccuracy of the dependency parsing results and the... | 2021.acl-long.494 | 10.18653/v1/2021.acl-long.494 | null | null | null |
2021.acl-long.495 | Multi-Label Few-Shot Learning for Aspect Category Detection | https://aclanthology.org/2021.acl-long.495/ | [
"Mengting Hu",
"Shiwan Zhao",
"Honglei Guo",
"Chao Xue",
"Hang Gao",
"Tiegang Gao",
"Renhong Cheng",
"Zhong Su"
] | Aspect category detection (ACD) in sentiment analysis aims to identify the aspect categories mentioned in a sentence. In this paper, we formulate ACD in the few-shot learning scenario. However, existing few-shot learning approaches mainly focus on single-label predictions. These methods can not work well for the ACD ta... | 2021.acl-long.495 | 10.18653/v1/2021.acl-long.495 | null | 2105.14174 | title_snapshot |
2021.acl-long.496 | Argument Pair Extraction via Attention-guided Multi-Layer Multi-Cross Encoding | https://aclanthology.org/2021.acl-long.496/ | [
"Liying Cheng",
"Tianyu Wu",
"Lidong Bing",
"Luo Si"
] | Argument pair extraction (APE) is a research task for extracting arguments from two passages and identifying potential argument pairs. Prior research work treats this task as a sequence labeling problem and a binary classification problem on two passages that are directly concatenated together, which has a limitation o... | 2021.acl-long.496 | 10.18653/v1/2021.acl-long.496 | null | null | null |
2021.acl-long.497 | A Neural Transition-based Model for Argumentation Mining | https://aclanthology.org/2021.acl-long.497/ | [
"Jianzhu Bao",
"Chuang Fan",
"Jipeng Wu",
"Yixue Dang",
"Jiachen Du",
"Ruifeng Xu"
] | The goal of argumentation mining is to automatically extract argumentation structures from argumentative texts. Most existing methods determine argumentative relations by exhaustively enumerating all possible pairs of argument components, which suffer from low efficiency and class imbalance. Moreover, due to the comple... | 2021.acl-long.497 | 10.18653/v1/2021.acl-long.497 | null | null | null |
2021.acl-long.498 | Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text | https://aclanthology.org/2021.acl-long.498/ | [
"Philippe Laban",
"Tobias Schnabel",
"Paul Bennett",
"Marti A. Hearst"
] | This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplicity. We train the model with a novel algorithm to optimize the reward (k-SCST), in which the model proposes several candidate simplifications... | 2021.acl-long.498 | 10.18653/v1/2021.acl-long.498 | null | 2107.03444 | title_snapshot |
2021.acl-long.499 | Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence | https://aclanthology.org/2021.acl-long.499/ | [
"Jian Guan",
"Xiaoxi Mao",
"Changjie Fan",
"Zitao Liu",
"Wenbiao Ding",
"Minlie Huang"
] | Generating long and coherent text is an important but challenging task, particularly for open-ended language generation tasks such as story generation. Despite the success in modeling intra-sentence coherence, existing generation models (e.g., BART) still struggle to maintain a coherent event sequence throughout the ge... | 2021.acl-long.499 | 10.18653/v1/2021.acl-long.499 | null | 2105.08963 | title_snapshot |
2021.acl-long.500 | OpenMEVA: A Benchmark for Evaluating Open-ended Story Generation Metrics | https://aclanthology.org/2021.acl-long.500/ | [
"Jian Guan",
"Zhexin Zhang",
"Zhuoer Feng",
"Zitao Liu",
"Wenbiao Ding",
"Xiaoxi Mao",
"Changjie Fan",
"Minlie Huang"
] | Automatic metrics are essential for developing natural language generation (NLG) models, particularly for open-ended language generation tasks such as story generation. However, existing automatic metrics are observed to correlate poorly with human evaluation. The lack of standardized benchmark datasets makes it diffic... | 2021.acl-long.500 | 10.18653/v1/2021.acl-long.500 | null | 2105.08920 | title_snapshot |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.