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 17 | title stringlengths 18 162 | paper_url stringlengths 41 43 | authors listlengths 1 21 | abstract large_stringlengths 252 1.89k | anthology_id stringlengths 15 17 | doi stringlengths 27 29 | award stringclasses 4
values | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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2020.acl-main.1 | Learning to Understand Child-directed and Adult-directed Speech | https://aclanthology.org/2020.acl-main.1/ | [
"Lieke Gelderloos",
"Grzegorz Chrupała",
"Afra Alishahi"
] | Speech directed to children differs from adult-directed speech in linguistic aspects such as repetition, word choice, and sentence length, as well as in aspects of the speech signal itself, such as prosodic and phonemic variation. Human language acquisition research indicates that child-directed speech helps language l... | 2020.acl-main.1 | 10.18653/v1/2020.acl-main.1 | null | 2005.02721 | title_snapshot |
2020.acl-main.2 | Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts | https://aclanthology.org/2020.acl-main.2/ | [
"Alex Rinaldi",
"Jean Fox Tree",
"Snigdha Chaturvedi"
] | Accurately diagnosing depression is difficult– requiring time-intensive interviews, assessments, and analysis. Hence, automated methods that can assess linguistic patterns in these interviews could help psychiatric professionals make faster, more informed decisions about diagnosis. We propose JLPC, a model that analyze... | 2020.acl-main.2 | 10.18653/v1/2020.acl-main.2 | null | null | null |
2020.acl-main.3 | Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling | https://aclanthology.org/2020.acl-main.3/ | [
"Zihan Liu",
"Genta Indra Winata",
"Peng Xu",
"Pascale Fung"
] | As an essential task in task-oriented dialog systems, slot filling requires extensive training data in a certain domain. However, such data are not always available. Hence, cross-domain slot filling has naturally arisen to cope with this data scarcity problem. In this paper, we propose a Coarse-to-fine approach (Coach)... | 2020.acl-main.3 | 10.18653/v1/2020.acl-main.3 | null | 2004.11727 | title_snapshot |
2020.acl-main.4 | Designing Precise and Robust Dialogue Response Evaluators | https://aclanthology.org/2020.acl-main.4/ | [
"Tianyu Zhao",
"Divesh Lala",
"Tatsuya Kawahara"
] | Automatic dialogue response evaluator has been proposed as an alternative to automated metrics and human evaluation. However, existing automatic evaluators achieve only moderate correlation with human judgement and they are not robust. In this work, we propose to build a reference-free evaluator and exploit the power o... | 2020.acl-main.4 | 10.18653/v1/2020.acl-main.4 | null | 2004.04908 | title_snapshot |
2020.acl-main.5 | Dialogue State Tracking with Explicit Slot Connection Modeling | https://aclanthology.org/2020.acl-main.5/ | [
"Yawen Ouyang",
"Moxin Chen",
"Xinyu Dai",
"Yinggong Zhao",
"Shujian Huang",
"Jiajun Chen"
] | Recent proposed approaches have made promising progress in dialogue state tracking (DST). However, in multi-domain scenarios, ellipsis and reference are frequently adopted by users to express values that have been mentioned by slots from other domains. To handle these phenomena, we propose a Dialogue State Tracking wit... | 2020.acl-main.5 | 10.18653/v1/2020.acl-main.5 | null | null | null |
2020.acl-main.6 | Generating Informative Conversational Response using Recurrent Knowledge-Interaction and Knowledge-Copy | https://aclanthology.org/2020.acl-main.6/ | [
"Xiexiong Lin",
"Weiyu Jian",
"Jianshan He",
"Taifeng Wang",
"Wei Chu"
] | Knowledge-driven conversation approaches have achieved remarkable research attention recently. However, generating an informative response with multiple relevant knowledge without losing fluency and coherence is still one of the main challenges. To address this issue, this paper proposes a method that uses recurrent kn... | 2020.acl-main.6 | 10.18653/v1/2020.acl-main.6 | null | null | null |
2020.acl-main.7 | Guiding Variational Response Generator to Exploit Persona | https://aclanthology.org/2020.acl-main.7/ | [
"Bowen Wu",
"MengYuan Li",
"Zongsheng Wang",
"Yifu Chen",
"Derek F. Wong",
"Qihang Feng",
"Junhong Huang",
"Baoxun Wang"
] | Leveraging persona information of users in Neural Response Generators (NRG) to perform personalized conversations has been considered as an attractive and important topic in the research of conversational agents over the past few years. Despite of the promising progress achieved by recent studies in this field, persona... | 2020.acl-main.7 | 10.18653/v1/2020.acl-main.7 | null | 1911.02390 | title_snapshot |
2020.acl-main.8 | Large Scale Multi-Actor Generative Dialog Modeling | https://aclanthology.org/2020.acl-main.8/ | [
"Alex Boyd",
"Raul Puri",
"Mohammad Shoeybi",
"Mostofa Patwary",
"Bryan Catanzaro"
] | Non-goal oriented dialog agents (i.e. chatbots) aim to produce varying and engaging conversations with a user; however, they typically exhibit either inconsistent personality across conversations or the average personality of all users. This paper addresses these issues by controlling an agent’s persona upon generation... | 2020.acl-main.8 | 10.18653/v1/2020.acl-main.8 | null | 2005.06114 | title_snapshot |
2020.acl-main.9 | PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable | https://aclanthology.org/2020.acl-main.9/ | [
"Siqi Bao",
"Huang He",
"Fan Wang",
"Hua Wu",
"Haifeng Wang"
] | Pre-training models have been proved effective for a wide range of natural language processing tasks. Inspired by this, we propose a novel dialogue generation pre-training framework to support various kinds of conversations, including chit-chat, knowledge grounded dialogues, and conversational question answering. In th... | 2020.acl-main.9 | 10.18653/v1/2020.acl-main.9 | null | 1910.07931 | title_snapshot |
2020.acl-main.10 | Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network | https://aclanthology.org/2020.acl-main.10/ | [
"Yangming Li",
"Kaisheng Yao",
"Libo Qin",
"Wanxiang Che",
"Xiaolong Li",
"Ting Liu"
] | Data-driven approaches using neural networks have achieved promising performances in natural language generation (NLG). However, neural generators are prone to make mistakes, e.g., neglecting an input slot value and generating a redundant slot value. Prior works refer this to hallucination phenomenon. In this paper, we... | 2020.acl-main.10 | 10.18653/v1/2020.acl-main.10 | null | null | null |
2020.acl-main.11 | Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations | https://aclanthology.org/2020.acl-main.11/ | [
"Samuel Coope",
"Tyler Farghly",
"Daniela Gerz",
"Ivan Vulić",
"Matthew Henderson"
] | We introduce Span-ConveRT, a light-weight model for dialog slot-filling which frames the task as a turn-based span extraction task. This formulation allows for a simple integration of conversational knowledge coded in large pretrained conversational models such as ConveRT (Henderson et al., 2019). We show that leveragi... | 2020.acl-main.11 | 10.18653/v1/2020.acl-main.11 | null | 2005.08866 | title_snapshot |
2020.acl-main.12 | Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking | https://aclanthology.org/2020.acl-main.12/ | [
"Giovanni Campagna",
"Agata Foryciarz",
"Mehrad Moradshahi",
"Monica Lam"
] | Zero-shot transfer learning for multi-domain dialogue state tracking can allow us to handle new domains without incurring the high cost of data acquisition. This paper proposes new zero-short transfer learning technique for dialogue state tracking where the in-domain training data are all synthesized from an abstract d... | 2020.acl-main.12 | 10.18653/v1/2020.acl-main.12 | null | 2005.00891 | title_snapshot |
2020.acl-main.13 | A Complete Shift-Reduce Chinese Discourse Parser with Robust Dynamic Oracle | https://aclanthology.org/2020.acl-main.13/ | [
"Shyh-Shiun Hung",
"Hen-Hsen Huang",
"Hsin-Hsi Chen"
] | This work proposes a standalone, complete Chinese discourse parser for practical applications. We approach Chinese discourse parsing from a variety of aspects and improve the shift-reduce parser not only by integrating the pre-trained text encoder, but also by employing novel training strategies. We revise the dynamic-... | 2020.acl-main.13 | 10.18653/v1/2020.acl-main.13 | null | null | null |
2020.acl-main.14 | TransS-Driven Joint Learning Architecture for Implicit Discourse Relation Recognition | https://aclanthology.org/2020.acl-main.14/ | [
"Ruifang He",
"Jian Wang",
"Fengyu Guo",
"Yugui Han"
] | Implicit discourse relation recognition is a challenging task due to the lack of connectives as strong linguistic clues. Previous methods primarily encode two arguments separately or extract the specific interaction patterns for the task, which have not fully exploited the annotated relation signal. Therefore, we propo... | 2020.acl-main.14 | 10.18653/v1/2020.acl-main.14 | null | null | null |
2020.acl-main.15 | A Study of Non-autoregressive Model for Sequence Generation | https://aclanthology.org/2020.acl-main.15/ | [
"Yi Ren",
"Jinglin Liu",
"Xu Tan",
"Zhou Zhao",
"Sheng Zhao",
"Tie-Yan Liu"
] | Non-autoregressive (NAR) models generate all the tokens of a sequence in parallel, resulting in faster generation speed compared to their autoregressive (AR) counterparts but at the cost of lower accuracy. Different techniques including knowledge distillation and source-target alignment have been proposed to bridge the... | 2020.acl-main.15 | 10.18653/v1/2020.acl-main.15 | null | 2004.10454 | title_snapshot |
2020.acl-main.16 | Cross-modal Language Generation using Pivot Stabilization for Web-scale Language Coverage | https://aclanthology.org/2020.acl-main.16/ | [
"Ashish V. Thapliyal",
"Radu Soricut"
] | Cross-modal language generation tasks such as image captioning are directly hurt in their ability to support non-English languages by the trend of data-hungry models combined with the lack of non-English annotations. We investigate potential solutions for combining existing language-generation annotations in English wi... | 2020.acl-main.16 | 10.18653/v1/2020.acl-main.16 | null | 2005.00246 | title_snapshot |
2020.acl-main.17 | Fact-based Text Editing | https://aclanthology.org/2020.acl-main.17/ | [
"Hayate Iso",
"Chao Qiao",
"Hang Li"
] | We propose a novel text editing task, referred to as fact-based text editing, in which the goal is to revise a given document to better describe the facts in a knowledge base (e.g., several triples). The task is important in practice because reflecting the truth is a common requirement in text editing. First, we propos... | 2020.acl-main.17 | 10.18653/v1/2020.acl-main.17 | null | 2007.00916 | title_snapshot |
2020.acl-main.18 | Few-Shot NLG with Pre-Trained Language Model | https://aclanthology.org/2020.acl-main.18/ | [
"Zhiyu Chen",
"Harini Eavani",
"Wenhu Chen",
"Yinyin Liu",
"William Yang Wang"
] | Neural-based end-to-end approaches to natural language generation (NLG) from structured data or knowledge are data-hungry, making their adoption for real-world applications difficult with limited data. In this work, we propose the new task of few-shot natural language generation. Motivated by how humans tend to summari... | 2020.acl-main.18 | 10.18653/v1/2020.acl-main.18 | null | 1904.09521 | title_snapshot |
2020.acl-main.19 | Fluent Response Generation for Conversational Question Answering | https://aclanthology.org/2020.acl-main.19/ | [
"Ashutosh Baheti",
"Alan Ritter",
"Kevin Small"
] | Question answering (QA) is an important aspect of open-domain conversational agents, garnering specific research focus in the conversational QA (ConvQA) subtask. One notable limitation of recent ConvQA efforts is the response being answer span extraction from the target corpus, thus ignoring the natural language genera... | 2020.acl-main.19 | 10.18653/v1/2020.acl-main.19 | null | 2005.10464 | title_snapshot |
2020.acl-main.20 | Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs | https://aclanthology.org/2020.acl-main.20/ | [
"Dong Bok Lee",
"Seanie Lee",
"Woo Tae Jeong",
"Donghwan Kim",
"Sung Ju Hwang"
] | One of the most crucial challenges in question answering (QA) is the scarcity of labeled data, since it is costly to obtain question-answer (QA) pairs for a target text domain with human annotation. An alternative approach to tackle the problem is to use automatically generated QA pairs from either the problem context ... | 2020.acl-main.20 | 10.18653/v1/2020.acl-main.20 | null | 2005.13837 | title_snapshot |
2020.acl-main.21 | Learning to Ask More: Semi-Autoregressive Sequential Question Generation under Dual-Graph Interaction | https://aclanthology.org/2020.acl-main.21/ | [
"Zi Chai",
"Xiaojun Wan"
] | Traditional Question Generation (TQG) aims to generate a question given an input passage and an answer. When there is a sequence of answers, we can perform Sequential Question Generation (SQG) to produce a series of interconnected questions. Since the frequently occurred information omission and coreference between que... | 2020.acl-main.21 | 10.18653/v1/2020.acl-main.21 | null | null | null |
2020.acl-main.22 | Neural Syntactic Preordering for Controlled Paraphrase Generation | https://aclanthology.org/2020.acl-main.22/ | [
"Tanya Goyal",
"Greg Durrett"
] | Paraphrasing natural language sentences is a multifaceted process: it might involve replacing individual words or short phrases, local rearrangement of content, or high-level restructuring like topicalization or passivization. Past approaches struggle to cover this space of paraphrase possibilities in an interpretable ... | 2020.acl-main.22 | 10.18653/v1/2020.acl-main.22 | null | 2005.02013 | title_snapshot |
2020.acl-main.23 | Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders | https://aclanthology.org/2020.acl-main.23/ | [
"Yu Duan",
"Canwen Xu",
"Jiaxin Pei",
"Jialong Han",
"Chenliang Li"
] | Conditional Text Generation has drawn much attention as a topic of Natural Language Generation (NLG) which provides the possibility for humans to control the properties of generated contents. Current conditional generation models cannot handle emerging conditions due to their joint end-to-end learning fashion. When a n... | 2020.acl-main.23 | 10.18653/v1/2020.acl-main.23 | null | 1911.03882 | title_snapshot |
2020.acl-main.24 | Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order | https://aclanthology.org/2020.acl-main.24/ | [
"Yi Liao",
"Xin Jiang",
"Qun Liu"
] | Masked language model and autoregressive language model are two types of language models. While pretrained masked language models such as BERT overwhelm the line of natural language understanding (NLU) tasks, autoregressive language models such as GPT are especially capable in natural language generation (NLG). In this... | 2020.acl-main.24 | 10.18653/v1/2020.acl-main.24 | null | 2004.11579 | title_snapshot |
2020.acl-main.25 | Reverse Engineering Configurations of Neural Text Generation Models | https://aclanthology.org/2020.acl-main.25/ | [
"Yi Tay",
"Dara Bahri",
"Che Zheng",
"Clifford Brunk",
"Donald Metzler",
"Andrew Tomkins"
] | Recent advances in neural text generation modeling have resulted in a number of societal concerns related to how such approaches might be used in malicious ways. It is therefore desirable to develop a deeper understanding of the fundamental properties of such models. The study of artifacts that emerge in machine genera... | 2020.acl-main.25 | 10.18653/v1/2020.acl-main.25 | null | 2004.06201 | title_snapshot |
2020.acl-main.26 | Review-based Question Generation with Adaptive Instance Transfer and Augmentation | https://aclanthology.org/2020.acl-main.26/ | [
"Qian Yu",
"Lidong Bing",
"Qiong Zhang",
"Wai Lam",
"Luo Si"
] | While online reviews of products and services become an important information source, it remains inefficient for potential consumers to exploit verbose reviews for fulfilling their information need. We propose to explore question generation as a new way of review information exploitation, namely generating questions th... | 2020.acl-main.26 | 10.18653/v1/2020.acl-main.26 | null | 1911.01556 | title_snapshot |
2020.acl-main.27 | TAG : Type Auxiliary Guiding for Code Comment Generation | https://aclanthology.org/2020.acl-main.27/ | [
"Ruichu Cai",
"Zhihao Liang",
"Boyan Xu",
"Zijian Li",
"Yuexing Hao",
"Yao Chen"
] | Existing leading code comment generation approaches with the structure-to-sequence framework ignores the type information of the interpretation of the code, e.g., operator, string, etc. However, introducing the type information into the existing framework is non-trivial due to the hierarchical dependence among the type... | 2020.acl-main.27 | 10.18653/v1/2020.acl-main.27 | null | 2005.02835 | title_snapshot |
2020.acl-main.28 | Unsupervised Paraphrasing by Simulated Annealing | https://aclanthology.org/2020.acl-main.28/ | [
"Xianggen Liu",
"Lili Mou",
"Fandong Meng",
"Hao Zhou",
"Jie Zhou",
"Sen Song"
] | We propose UPSA, a novel approach that accomplishes Unsupervised Paraphrasing by Simulated Annealing. We model paraphrase generation as an optimization problem and propose a sophisticated objective function, involving semantic similarity, expression diversity, and language fluency of paraphrases. UPSA searches the sent... | 2020.acl-main.28 | 10.18653/v1/2020.acl-main.28 | null | 1909.03588 | title_snapshot |
2020.acl-main.29 | A Joint Model for Document Segmentation and Segment Labeling | https://aclanthology.org/2020.acl-main.29/ | [
"Joe Barrow",
"Rajiv Jain",
"Vlad Morariu",
"Varun Manjunatha",
"Douglas Oard",
"Philip Resnik"
] | Text segmentation aims to uncover latent structure by dividing text from a document into coherent sections. Where previous work on text segmentation considers the tasks of document segmentation and segment labeling separately, we show that the tasks contain complementary information and are best addressed jointly. We i... | 2020.acl-main.29 | 10.18653/v1/2020.acl-main.29 | null | null | null |
2020.acl-main.30 | Contextualized Weak Supervision for Text Classification | https://aclanthology.org/2020.acl-main.30/ | [
"Dheeraj Mekala",
"Jingbo Shang"
] | Weakly supervised text classification based on a few user-provided seed words has recently attracted much attention from researchers. Existing methods mainly generate pseudo-labels in a context-free manner (e.g., string matching), therefore, the ambiguous, context-dependent nature of human language has been long overlo... | 2020.acl-main.30 | 10.18653/v1/2020.acl-main.30 | null | null | null |
2020.acl-main.31 | Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks | https://aclanthology.org/2020.acl-main.31/ | [
"Yufeng Zhang",
"Xueli Yu",
"Zeyu Cui",
"Shu Wu",
"Zhongzhen Wen",
"Liang Wang"
] | Text classification is fundamental in natural language processing (NLP) and Graph Neural Networks (GNN) are recently applied in this task. However, the existing graph-based works can neither capture the contextual word relationships within each document nor fulfil the inductive learning of new words. Therefore in this ... | 2020.acl-main.31 | 10.18653/v1/2020.acl-main.31 | null | 2004.13826 | title_snapshot |
2020.acl-main.32 | Neural Topic Modeling with Bidirectional Adversarial Training | https://aclanthology.org/2020.acl-main.32/ | [
"Rui Wang",
"Xuemeng Hu",
"Deyu Zhou",
"Yulan He",
"Yuxuan Xiong",
"Chenchen Ye",
"Haiyang Xu"
] | Recent years have witnessed a surge of interests of using neural topic models for automatic topic extraction from text, since they avoid the complicated mathematical derivations for model inference as in traditional topic models such as Latent Dirichlet Allocation (LDA). However, these models either typically assume im... | 2020.acl-main.32 | 10.18653/v1/2020.acl-main.32 | null | 2004.12331 | title_snapshot |
2020.acl-main.33 | Text Classification with Negative Supervision | https://aclanthology.org/2020.acl-main.33/ | [
"Sora Ohashi",
"Junya Takayama",
"Tomoyuki Kajiwara",
"Chenhui Chu",
"Yuki Arase"
] | Advanced pre-trained models for text representation have achieved state-of-the-art performance on various text classification tasks. However, the discrepancy between the semantic similarity of texts and labelling standards affects classifiers, i.e. leading to lower performance in cases where classifiers should assign d... | 2020.acl-main.33 | 10.18653/v1/2020.acl-main.33 | null | null | null |
2020.acl-main.34 | Content Word Aware Neural Machine Translation | https://aclanthology.org/2020.acl-main.34/ | [
"Kehai Chen",
"Rui Wang",
"Masao Utiyama",
"Eiichiro Sumita"
] | Neural machine translation (NMT) encodes the source sentence in a universal way to generate the target sentence word-by-word. However, NMT does not consider the importance of word in the sentence meaning, for example, some words (i.e., content words) express more important meaning than others (i.e., function words). To... | 2020.acl-main.34 | 10.18653/v1/2020.acl-main.34 | null | null | null |
2020.acl-main.35 | Evaluating Explanation Methods for Neural Machine Translation | https://aclanthology.org/2020.acl-main.35/ | [
"Jierui Li",
"Lemao Liu",
"Huayang Li",
"Guanlin Li",
"Guoping Huang",
"Shuming Shi"
] | Recently many efforts have been devoted to interpreting the black-box NMT models, but little progress has been made on metrics to evaluate explanation methods. Word Alignment Error Rate can be used as such a metric that matches human understanding, however, it can not measure explanation methods on those target words t... | 2020.acl-main.35 | 10.18653/v1/2020.acl-main.35 | null | 2005.01672 | title_snapshot |
2020.acl-main.36 | Jointly Masked Sequence-to-Sequence Model for Non-Autoregressive Neural Machine Translation | https://aclanthology.org/2020.acl-main.36/ | [
"Junliang Guo",
"Linli Xu",
"Enhong Chen"
] | The masked language model has received remarkable attention due to its effectiveness on various natural language processing tasks. However, few works have adopted this technique in the sequence-to-sequence models. In this work, we introduce a jointly masked sequence-to-sequence model and explore its application on non-... | 2020.acl-main.36 | 10.18653/v1/2020.acl-main.36 | null | null | null |
2020.acl-main.37 | Learning Source Phrase Representations for Neural Machine Translation | https://aclanthology.org/2020.acl-main.37/ | [
"Hongfei Xu",
"Josef van Genabith",
"Deyi Xiong",
"Qiuhui Liu",
"Jingyi Zhang"
] | The Transformer translation model (Vaswani et al., 2017) based on a multi-head attention mechanism can be computed effectively in parallel and has significantly pushed forward the performance of Neural Machine Translation (NMT). Though intuitively the attentional network can connect distant words via shorter network pa... | 2020.acl-main.37 | 10.18653/v1/2020.acl-main.37 | null | 2006.14405 | title_snapshot |
2020.acl-main.38 | Lipschitz Constrained Parameter Initialization for Deep Transformers | https://aclanthology.org/2020.acl-main.38/ | [
"Hongfei Xu",
"Qiuhui Liu",
"Josef van Genabith",
"Deyi Xiong",
"Jingyi Zhang"
] | The Transformer translation model employs residual connection and layer normalization to ease the optimization difficulties caused by its multi-layer encoder/decoder structure. Previous research shows that even with residual connection and layer normalization, deep Transformers still have difficulty in training, and pa... | 2020.acl-main.38 | 10.18653/v1/2020.acl-main.38 | null | 1911.03179 | title_snapshot |
2020.acl-main.39 | Location Attention for Extrapolation to Longer Sequences | https://aclanthology.org/2020.acl-main.39/ | [
"Yann Dubois",
"Gautier Dagan",
"Dieuwke Hupkes",
"Elia Bruni"
] | Neural networks are surprisingly good at interpolating and perform remarkably well when the training set examples resemble those in the test set. However, they are often unable to extrapolate patterns beyond the seen data, even when the abstractions required for such patterns are simple. In this paper, we first review ... | 2020.acl-main.39 | 10.18653/v1/2020.acl-main.39 | null | 1911.03872 | title_snapshot |
2020.acl-main.40 | Multiscale Collaborative Deep Models for Neural Machine Translation | https://aclanthology.org/2020.acl-main.40/ | [
"Xiangpeng Wei",
"Heng Yu",
"Yue Hu",
"Yue Zhang",
"Rongxiang Weng",
"Weihua Luo"
] | Recent evidence reveals that Neural Machine Translation (NMT) models with deeper neural networks can be more effective but are difficult to train. In this paper, we present a MultiScale Collaborative (MSC) framework to ease the training of NMT models that are substantially deeper than those used previously. We explicit... | 2020.acl-main.40 | 10.18653/v1/2020.acl-main.40 | null | 2004.14021 | title_snapshot |
2020.acl-main.41 | Norm-Based Curriculum Learning for Neural Machine Translation | https://aclanthology.org/2020.acl-main.41/ | [
"Xuebo Liu",
"Houtim Lai",
"Derek F. Wong",
"Lidia S. Chao"
] | A neural machine translation (NMT) system is expensive to train, especially with high-resource settings. As the NMT architectures become deeper and wider, this issue gets worse and worse. In this paper, we aim to improve the efficiency of training an NMT by introducing a novel norm-based curriculum learning method. We ... | 2020.acl-main.41 | 10.18653/v1/2020.acl-main.41 | null | 2006.02014 | title_snapshot |
2020.acl-main.42 | Opportunistic Decoding with Timely Correction for Simultaneous Translation | https://aclanthology.org/2020.acl-main.42/ | [
"Renjie Zheng",
"Mingbo Ma",
"Baigong Zheng",
"Kaibo Liu",
"Liang Huang"
] | Simultaneous translation has many important application scenarios and attracts much attention from both academia and industry recently. Most existing frameworks, however, have difficulties in balancing between the translation quality and latency, i.e., the decoding policy is usually either too aggressive or too conserv... | 2020.acl-main.42 | 10.18653/v1/2020.acl-main.42 | null | 2005.00675 | title_snapshot |
2020.acl-main.43 | A Formal Hierarchy of RNN Architectures | https://aclanthology.org/2020.acl-main.43/ | [
"William Merrill",
"Gail Weiss",
"Yoav Goldberg",
"Roy Schwartz",
"Noah A. Smith",
"Eran Yahav"
] | We develop a formal hierarchy of the expressive capacity of RNN architectures. The hierarchy is based on two formal properties: space complexity, which measures the RNN’s memory, and rational recurrence, defined as whether the recurrent update can be described by a weighted finite-state machine. We place several RNN va... | 2020.acl-main.43 | 10.18653/v1/2020.acl-main.43 | null | 2004.08500 | title_snapshot |
2020.acl-main.44 | A Three-Parameter Rank-Frequency Relation in Natural Languages | https://aclanthology.org/2020.acl-main.44/ | [
"Chenchen Ding",
"Masao Utiyama",
"Eiichiro Sumita"
] | We present that, the rank-frequency relation in textual data follows f \propto r^{-\alpha}(r+\gamma)^{-\beta}, where f is the token frequency and r is the rank by frequency, with (\alpha, \beta, \gamma) as parameters. The formulation is derived based on the empirical observation that d^2 (x+y)/dx^2 is a typical impulse... | 2020.acl-main.44 | 10.18653/v1/2020.acl-main.44 | null | null | null |
2020.acl-main.45 | Dice Loss for Data-imbalanced NLP Tasks | https://aclanthology.org/2020.acl-main.45/ | [
"Xiaoya Li",
"Xiaofei Sun",
"Yuxian Meng",
"Junjun Liang",
"Fei Wu",
"Jiwei Li"
] | Many NLP tasks such as tagging and machine reading comprehension are faced with the severe data imbalance issue: negative examples significantly outnumber positive examples, and the huge number of easy-negative examples overwhelms the training. The most commonly used cross entropy (CE) criteria is actually an accuracy-... | 2020.acl-main.45 | 10.18653/v1/2020.acl-main.45 | null | 1911.02855 | title_snapshot |
2020.acl-main.46 | Emergence of Syntax Needs Minimal Supervision | https://aclanthology.org/2020.acl-main.46/ | [
"Raphaël Bailly",
"Kata Gábor"
] | This paper is a theoretical contribution to the debate on the learnability of syntax from a corpus without explicit syntax-specific guidance. Our approach originates in the observable structure of a corpus, which we use to define and isolate grammaticality (syntactic information) and meaning/pragmatics information. We ... | 2020.acl-main.46 | 10.18653/v1/2020.acl-main.46 | null | 2005.01119 | title_snapshot |
2020.acl-main.47 | Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese | https://aclanthology.org/2020.acl-main.47/ | [
"Tatsuki Kuribayashi",
"Takumi Ito",
"Jun Suzuki",
"Kentaro Inui"
] | We examine a methodology using neural language models (LMs) for analyzing the word order of language. This LM-based method has the potential to overcome the difficulties existing methods face, such as the propagation of preprocessor errors in count-based methods. In this study, we explore whether the LM-based method is... | 2020.acl-main.47 | 10.18653/v1/2020.acl-main.47 | null | 2005.00842 | title_snapshot |
2020.acl-main.48 | GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media | https://aclanthology.org/2020.acl-main.48/ | [
"Yi-Ju Lu",
"Cheng-Te Li"
] | This paper solves the fake news detection problem under a more realistic scenario on social media. Given the source short-text tweet and the corresponding sequence of retweet users without text comments, we aim at predicting whether the source tweet is fake or not, and generating explanation by highlighting the evidenc... | 2020.acl-main.48 | 10.18653/v1/2020.acl-main.48 | null | 2004.11648 | title_snapshot |
2020.acl-main.49 | Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection | https://aclanthology.org/2020.acl-main.49/ | [
"Lei Zhong",
"Juan Cao",
"Qiang Sheng",
"Junbo Guo",
"Ziang Wang"
] | Identifying controversial posts on social media is a fundamental task for mining public sentiment, assessing the influence of events, and alleviating the polarized views. However, existing methods fail to 1) effectively incorporate the semantic information from content-related posts; 2) preserve the structural informat... | 2020.acl-main.49 | 10.18653/v1/2020.acl-main.49 | null | 2005.07886 | title_snapshot |
2020.acl-main.50 | Predicting the Topical Stance and Political Leaning of Media using Tweets | https://aclanthology.org/2020.acl-main.50/ | [
"Peter Stefanov",
"Kareem Darwish",
"Atanas Atanasov",
"Preslav Nakov"
] | Discovering the stances of media outlets and influential people on current, debatable topics is important for social statisticians and policy makers. Many supervised solutions exist for determining viewpoints, but manually annotating training data is costly. In this paper, we propose a cascaded method that uses unsuper... | 2020.acl-main.50 | 10.18653/v1/2020.acl-main.50 | null | 1907.01260 | title_judge |
2020.acl-main.51 | Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora | https://aclanthology.org/2020.acl-main.51/ | [
"Hila Gonen",
"Ganesh Jawahar",
"Djamé Seddah",
"Yoav Goldberg"
] | The problem of comparing two bodies of text and searching for words that differ in their usage between them arises often in digital humanities and computational social science. This is commonly approached by training word embeddings on each corpus, aligning the vector spaces, and looking for words whose cosine distance... | 2020.acl-main.51 | 10.18653/v1/2020.acl-main.51 | null | 2112.14330 | title_snapshot |
2020.acl-main.52 | CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation | https://aclanthology.org/2020.acl-main.52/ | [
"Lei Shen",
"Yang Feng"
] | Emotion-controllable response generation is an attractive and valuable task that aims to make open-domain conversations more empathetic and engaging. Existing methods mainly enhance the emotion expression by adding regularization terms to standard cross-entropy loss and thus influence the training process. However, due... | 2020.acl-main.52 | 10.18653/v1/2020.acl-main.52 | null | 2005.00329 | title_snapshot |
2020.acl-main.53 | Efficient Dialogue State Tracking by Selectively Overwriting Memory | https://aclanthology.org/2020.acl-main.53/ | [
"Sungdong Kim",
"Sohee Yang",
"Gyuwan Kim",
"Sang-Woo Lee"
] | Recent works in dialogue state tracking (DST) focus on an open vocabulary-based setting to resolve scalability and generalization issues of the predefined ontology-based approaches. However, they are inefficient in that they predict the dialogue state at every turn from scratch. Here, we consider dialogue state as an e... | 2020.acl-main.53 | 10.18653/v1/2020.acl-main.53 | null | 1911.03906 | title_snapshot |
2020.acl-main.54 | End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2 | https://aclanthology.org/2020.acl-main.54/ | [
"Donghoon Ham",
"Jeong-Gwan Lee",
"Youngsoo Jang",
"Kee-Eung Kim"
] | The goal-oriented dialogue system needs to be optimized for tracking the dialogue flow and carrying out an effective conversation under various situations to meet the user goal. The traditional approach to build such a dialogue system is to take a pipelined modular architecture, where its modules are optimized individu... | 2020.acl-main.54 | 10.18653/v1/2020.acl-main.54 | null | null | null |
2020.acl-main.55 | Evaluating Dialogue Generation Systems via Response Selection | https://aclanthology.org/2020.acl-main.55/ | [
"Shiki Sato",
"Reina Akama",
"Hiroki Ouchi",
"Jun Suzuki",
"Kentaro Inui"
] | Existing automatic evaluation metrics for open-domain dialogue response generation systems correlate poorly with human evaluation. We focus on evaluating response generation systems via response selection. To evaluate systems properly via response selection, we propose a method to construct response selection test sets... | 2020.acl-main.55 | 10.18653/v1/2020.acl-main.55 | null | 2004.14302 | title_snapshot |
2020.acl-main.56 | Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response Detection | https://aclanthology.org/2020.acl-main.56/ | [
"Yefei Zha",
"Ruobing Li",
"Hui Lin"
] | Off-topic spoken response detection, the task aiming at predicting whether a response is off-topic for the corresponding prompt, is important for an automated speaking assessment system. In many real-world educational applications, off-topic spoken response detectors are required to achieve high recall for off-topic re... | 2020.acl-main.56 | 10.18653/v1/2020.acl-main.56 | null | 2004.09036 | title_snapshot |
2020.acl-main.57 | Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment | https://aclanthology.org/2020.acl-main.57/ | [
"Yinpei Dai",
"Hangyu Li",
"Chengguang Tang",
"Yongbin Li",
"Jian Sun",
"Xiaodan Zhu"
] | Existing end-to-end dialog systems perform less effectively when data is scarce. To obtain an acceptable success in real-life online services with only a handful of training examples, both fast adaptability and reliable performance are highly desirable for dialog systems. In this paper, we propose the Meta-Dialog Syste... | 2020.acl-main.57 | 10.18653/v1/2020.acl-main.57 | null | null | null |
2020.acl-main.58 | Learning to Tag OOV Tokens by Integrating Contextual Representation and Background Knowledge | https://aclanthology.org/2020.acl-main.58/ | [
"Keqing He",
"Yuanmeng Yan",
"Weiran Xu"
] | Neural-based context-aware models for slot tagging have achieved state-of-the-art performance. However, the presence of OOV(out-of-vocab) words significantly degrades the performance of neural-based models, especially in a few-shot scenario. In this paper, we propose a novel knowledge-enhanced slot tagging model to int... | 2020.acl-main.58 | 10.18653/v1/2020.acl-main.58 | null | null | null |
2020.acl-main.59 | Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition | https://aclanthology.org/2020.acl-main.59/ | [
"Ryuichi Takanobu",
"Runze Liang",
"Minlie Huang"
] | Many studies have applied reinforcement learning to train a dialog policy and show great promise these years. One common approach is to employ a user simulator to obtain a large number of simulated user experiences for reinforcement learning algorithms. However, modeling a realistic user simulator is challenging. A rul... | 2020.acl-main.59 | 10.18653/v1/2020.acl-main.59 | null | 2004.03809 | title_snapshot |
2020.acl-main.60 | Paraphrase Augmented Task-Oriented Dialog Generation | https://aclanthology.org/2020.acl-main.60/ | [
"Silin Gao",
"Yichi Zhang",
"Zhijian Ou",
"Zhou Yu"
] | Neural generative models have achieved promising performance on dialog generation tasks if given a huge data set. However, the lack of high-quality dialog data and the expensive data annotation process greatly limit their application in real world settings. We propose a paraphrase augmented response generation (PARG) f... | 2020.acl-main.60 | 10.18653/v1/2020.acl-main.60 | null | 2004.07462 | title_snapshot |
2020.acl-main.61 | Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation | https://aclanthology.org/2020.acl-main.61/ | [
"Zhiliang Tian",
"Wei Bi",
"Dongkyu Lee",
"Lanqing Xue",
"Yiping Song",
"Xiaojiang Liu",
"Nevin L. Zhang"
] | Neural conversation models are known to generate appropriate but non-informative responses in general. A scenario where informativeness can be significantly enhanced is Conversing by Reading (CbR), where conversations take place with respect to a given external document. In previous work, the external document is utili... | 2020.acl-main.61 | 10.18653/v1/2020.acl-main.61 | null | 2005.06128 | title_snapshot |
2020.acl-main.62 | Semi-Supervised Dialogue Policy Learning via Stochastic Reward Estimation | https://aclanthology.org/2020.acl-main.62/ | [
"Xinting Huang",
"Jianzhong Qi",
"Yu Sun",
"Rui Zhang"
] | Dialogue policy optimization often obtains feedback until task completion in task-oriented dialogue systems. This is insufficient for training intermediate dialogue turns since supervision signals (or rewards) are only provided at the end of dialogues. To address this issue, reward learning has been introduced to learn... | 2020.acl-main.62 | 10.18653/v1/2020.acl-main.62 | null | 2005.04379 | title_snapshot |
2020.acl-main.63 | Towards Unsupervised Language Understanding and Generation by Joint Dual Learning | https://aclanthology.org/2020.acl-main.63/ | [
"Shang-Yu Su",
"Chao-Wei Huang",
"Yun-Nung Chen"
] | In modular dialogue systems, natural language understanding (NLU) and natural language generation (NLG) are two critical components, where NLU extracts the semantics from the given texts and NLG is to construct corresponding natural language sentences based on the input semantic representations. However, the dual prope... | 2020.acl-main.63 | 10.18653/v1/2020.acl-main.63 | null | 2004.14710 | title_snapshot |
2020.acl-main.64 | USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation | https://aclanthology.org/2020.acl-main.64/ | [
"Shikib Mehri",
"Maxine Eskenazi"
] | The lack of meaningful automatic evaluation metrics for dialog has impeded open-domain dialog research. Standard language generation metrics have been shown to be ineffective for evaluating dialog models. To this end, this paper presents USR, an UnSupervised and Reference-free evaluation metric for dialog. USR is a ref... | 2020.acl-main.64 | 10.18653/v1/2020.acl-main.64 | null | 2005.00456 | title_snapshot |
2020.acl-main.65 | Explicit Semantic Decomposition for Definition Generation | https://aclanthology.org/2020.acl-main.65/ | [
"Jiahuan Li",
"Yu Bao",
"Shujian Huang",
"Xinyu Dai",
"Jiajun Chen"
] | Definition generation, which aims to automatically generate dictionary definitions for words, has recently been proposed to assist the construction of dictionaries and help people understand unfamiliar texts. However, previous works hardly consider explicitly modeling the “components” of definitions, leading to under-s... | 2020.acl-main.65 | 10.18653/v1/2020.acl-main.65 | null | null | null |
2020.acl-main.66 | Improved Natural Language Generation via Loss Truncation | https://aclanthology.org/2020.acl-main.66/ | [
"Daniel Kang",
"Tatsunori B. Hashimoto"
] | Neural language models are usually trained to match the distributional properties of large-scale corpora by minimizing the log loss. While straightforward to optimize, this approach forces the model to reproduce all variations in the dataset, including noisy and invalid references (e.g., misannotations and hallucinated... | 2020.acl-main.66 | 10.18653/v1/2020.acl-main.66 | null | 2004.14589 | title_snapshot |
2020.acl-main.67 | Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks | https://aclanthology.org/2020.acl-main.67/ | [
"Yanbin Zhao",
"Lu Chen",
"Zhi Chen",
"Ruisheng Cao",
"Su Zhu",
"Kai Yu"
] | Efficient structure encoding for graphs with labeled edges is an important yet challenging point in many graph-based models. This work focuses on AMR-to-text generation – A graph-to-sequence task aiming to recover natural language from Abstract Meaning Representations (AMR). Existing graph-to-sequence approaches genera... | 2020.acl-main.67 | 10.18653/v1/2020.acl-main.67 | null | null | null |
2020.acl-main.68 | Rigid Formats Controlled Text Generation | https://aclanthology.org/2020.acl-main.68/ | [
"Piji Li",
"Haisong Zhang",
"Xiaojiang Liu",
"Shuming Shi"
] | Neural text generation has made tremendous progress in various tasks. One common characteristic of most of the tasks is that the texts are not restricted to some rigid formats when generating. However, we may confront some special text paradigms such as Lyrics (assume the music score is given), Sonnet, SongCi (classica... | 2020.acl-main.68 | 10.18653/v1/2020.acl-main.68 | null | 2004.08022 | title_judge |
2020.acl-main.69 | Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation | https://aclanthology.org/2020.acl-main.69/ | [
"Kaustubh Dhole",
"Christopher D. Manning"
] | Question Generation (QG) is fundamentally a simple syntactic transformation; however, many aspects of semantics influence what questions are good to form. We implement this observation by developing Syn-QG, a set of transparent syntactic rules leveraging universal dependencies, shallow semantic parsing, lexical resourc... | 2020.acl-main.69 | 10.18653/v1/2020.acl-main.69 | null | 2004.08694 | title_snapshot |
2020.acl-main.70 | An Online Semantic-enhanced Dirichlet Model for Short Text Stream Clustering | https://aclanthology.org/2020.acl-main.70/ | [
"Jay Kumar",
"Junming Shao",
"Salah Uddin",
"Wazir Ali"
] | Clustering short text streams is a challenging task due to its unique properties: infinite length, sparse data representation and cluster evolution. Existing approaches often exploit short text streams in a batch way. However, determine the optimal batch size is usually a difficult task since we have no priori knowledg... | 2020.acl-main.70 | 10.18653/v1/2020.acl-main.70 | null | null | null |
2020.acl-main.71 | Generative Semantic Hashing Enhanced via Boltzmann Machines | https://aclanthology.org/2020.acl-main.71/ | [
"Lin Zheng",
"Qinliang Su",
"Dinghan Shen",
"Changyou Chen"
] | Generative semantic hashing is a promising technique for large-scale information retrieval thanks to its fast retrieval speed and small memory footprint. For the tractability of training, existing generative-hashing methods mostly assume a factorized form for the posterior distribution, enforcing independence among the... | 2020.acl-main.71 | 10.18653/v1/2020.acl-main.71 | null | 2006.08858 | title_snapshot |
2020.acl-main.72 | Interactive Construction of User-Centric Dictionary for Text Analytics | https://aclanthology.org/2020.acl-main.72/ | [
"Ryosuke Kohita",
"Issei Yoshida",
"Hiroshi Kanayama",
"Tetsuya Nasukawa"
] | We propose a methodology to construct a term dictionary for text analytics through an interactive process between a human and a machine, which helps the creation of flexible dictionaries with precise granularity required in typical text analysis. This paper introduces the first formulation of interactive dictionary con... | 2020.acl-main.72 | 10.18653/v1/2020.acl-main.72 | null | null | null |
2020.acl-main.73 | Tree-Structured Neural Topic Model | https://aclanthology.org/2020.acl-main.73/ | [
"Masaru Isonuma",
"Junichiro Mori",
"Danushka Bollegala",
"Ichiro Sakata"
] | This paper presents a tree-structured neural topic model, which has a topic distribution over a tree with an infinite number of branches. Our model parameterizes an unbounded ancestral and fraternal topic distribution by applying doubly-recurrent neural networks. With the help of autoencoding variational Bayes, our mod... | 2020.acl-main.73 | 10.18653/v1/2020.acl-main.73 | null | null | null |
2020.acl-main.74 | Unsupervised FAQ Retrieval with Question Generation and BERT | https://aclanthology.org/2020.acl-main.74/ | [
"Yosi Mass",
"Boaz Carmeli",
"Haggai Roitman",
"David Konopnicki"
] | We focus on the task of Frequently Asked Questions (FAQ) retrieval. A given user query can be matched against the questions and/or the answers in the FAQ. We present a fully unsupervised method that exploits the FAQ pairs to train two BERT models. The two models match user queries to FAQ answers and questions, respecti... | 2020.acl-main.74 | 10.18653/v1/2020.acl-main.74 | null | null | null |
2020.acl-main.75 | “The Boating Store Had Its Best Sail Ever”: Pronunciation-attentive Contextualized Pun Recognition | https://aclanthology.org/2020.acl-main.75/ | [
"Yichao Zhou",
"Jyun-Yu Jiang",
"Jieyu Zhao",
"Kai-Wei Chang",
"Wei Wang"
] | Humor plays an important role in human languages and it is essential to model humor when building intelligence systems. Among different forms of humor, puns perform wordplay for humorous effects by employing words with double entendre and high phonetic similarity. However, identifying and modeling puns are challenging ... | 2020.acl-main.75 | 10.18653/v1/2020.acl-main.75 | null | 2004.14457 | title_snapshot |
2020.acl-main.76 | Fast and Accurate Deep Bidirectional Language Representations for Unsupervised Learning | https://aclanthology.org/2020.acl-main.76/ | [
"Joongbo Shin",
"Yoonhyung Lee",
"Seunghyun Yoon",
"Kyomin Jung"
] | Even though BERT has achieved successful performance improvements in various supervised learning tasks, BERT is still limited by repetitive inferences on unsupervised tasks for the computation of contextual language representations. To resolve this limitation, we propose a novel deep bidirectional language model called... | 2020.acl-main.76 | 10.18653/v1/2020.acl-main.76 | null | 2004.08097 | title_snapshot |
2020.acl-main.77 | Fine-grained Interest Matching for Neural News Recommendation | https://aclanthology.org/2020.acl-main.77/ | [
"Heyuan Wang",
"Fangzhao Wu",
"Zheng Liu",
"Xing Xie"
] | Personalized news recommendation is a critical technology to improve users’ online news reading experience. The core of news recommendation is accurate matching between user’s interests and candidate news. The same user usually has diverse interests that are reflected in different news she has browsed. Meanwhile, impor... | 2020.acl-main.77 | 10.18653/v1/2020.acl-main.77 | null | null | null |
2020.acl-main.78 | Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder | https://aclanthology.org/2020.acl-main.78/ | [
"Fan Zhou",
"Shengming Zhang",
"Yi Yang"
] | Operational risk management is one of the biggest challenges nowadays faced by financial institutions. There are several major challenges of building a text classification system for automatic operational risk prediction, including imbalanced labeled/unlabeled data and lacking interpretability. To tackle these challeng... | 2020.acl-main.78 | 10.18653/v1/2020.acl-main.78 | null | null | null |
2020.acl-main.79 | Interpreting Twitter User Geolocation | https://aclanthology.org/2020.acl-main.79/ | [
"Ting Zhong",
"Tianliang Wang",
"Fan Zhou",
"Goce Trajcevski",
"Kunpeng Zhang",
"Yi Yang"
] | Identifying user geolocation in online social networks is an essential task in many location-based applications. Existing methods rely on the similarity of text and network structure, however, they suffer from a lack of interpretability on the corresponding results, which is crucial for understanding model behavior. In... | 2020.acl-main.79 | 10.18653/v1/2020.acl-main.79 | null | null | null |
2020.acl-main.80 | Modeling Code-Switch Languages Using Bilingual Parallel Corpus | https://aclanthology.org/2020.acl-main.80/ | [
"Grandee Lee",
"Haizhou Li"
] | Language modeling is the technique to estimate the probability of a sequence of words. A bilingual language model is expected to model the sequential dependency for words across languages, which is difficult due to the inherent lack of suitable training data as well as diverse syntactic structure across languages. We p... | 2020.acl-main.80 | 10.18653/v1/2020.acl-main.80 | null | null | null |
2020.acl-main.81 | SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check | https://aclanthology.org/2020.acl-main.81/ | [
"Xingyi Cheng",
"Weidi Xu",
"Kunlong Chen",
"Shaohua Jiang",
"Feng Wang",
"Taifeng Wang",
"Wei Chu",
"Yuan Qi"
] | Chinese Spelling Check (CSC) is a task to detect and correct spelling errors in Chinese natural language. Existing methods have made attempts to incorporate the similarity knowledge between Chinese characters. However, they take the similarity knowledge as either an external input resource or just heuristic rules. This... | 2020.acl-main.81 | 10.18653/v1/2020.acl-main.81 | null | 2004.14166 | title_snapshot |
2020.acl-main.82 | Spelling Error Correction with Soft-Masked BERT | https://aclanthology.org/2020.acl-main.82/ | [
"Shaohua Zhang",
"Haoran Huang",
"Jicong Liu",
"Hang Li"
] | Spelling error correction is an important yet challenging task because a satisfactory solution of it essentially needs human-level language understanding ability. Without loss of generality we consider Chinese spelling error correction (CSC) in this paper. A state-of-the-art method for the task selects a character from... | 2020.acl-main.82 | 10.18653/v1/2020.acl-main.82 | null | 2005.07421 | title_snapshot |
2020.acl-main.83 | A Frame-based Sentence Representation for Machine Reading Comprehension | https://aclanthology.org/2020.acl-main.83/ | [
"Shaoru Guo",
"Ru Li",
"Hongye Tan",
"Xiaoli Li",
"Yong Guan",
"Hongyan Zhao",
"Yueping Zhang"
] | Sentence representation (SR) is the most crucial and challenging task in Machine Reading Comprehension (MRC). MRC systems typically only utilize the information contained in the sentence itself, while human beings can leverage their semantic knowledge. To bridge the gap, we proposed a novel Frame-based Sentence Represe... | 2020.acl-main.83 | 10.18653/v1/2020.acl-main.83 | null | null | null |
2020.acl-main.84 | A Methodology for Creating Question Answering Corpora Using Inverse Data Annotation | https://aclanthology.org/2020.acl-main.84/ | [
"Jan Deriu",
"Katsiaryna Mlynchyk",
"Philippe Schläpfer",
"Alvaro Rodrigo",
"Dirk von Grünigen",
"Nicolas Kaiser",
"Kurt Stockinger",
"Eneko Agirre",
"Mark Cieliebak"
] | In this paper, we introduce a novel methodology to efficiently construct a corpus for question answering over structured data. For this, we introduce an intermediate representation that is based on the logical query plan in a database, called Operation Trees (OT). This representation allows us to invert the annotation ... | 2020.acl-main.84 | 10.18653/v1/2020.acl-main.84 | null | 2004.07633 | title_snapshot |
2020.acl-main.85 | Contextualized Sparse Representations for Real-Time Open-Domain Question Answering | https://aclanthology.org/2020.acl-main.85/ | [
"Jinhyuk Lee",
"Minjoon Seo",
"Hannaneh Hajishirzi",
"Jaewoo Kang"
] | Open-domain question answering can be formulated as a phrase retrieval problem, in which we can expect huge scalability and speed benefit but often suffer from low accuracy due to the limitation of existing phrase representation models. In this paper, we aim to improve the quality of each phrase embedding by augmenting... | 2020.acl-main.85 | 10.18653/v1/2020.acl-main.85 | null | 1911.02896 | title_snapshot |
2020.acl-main.86 | Dynamic Sampling Strategies for Multi-Task Reading Comprehension | https://aclanthology.org/2020.acl-main.86/ | [
"Ananth Gottumukkala",
"Dheeru Dua",
"Sameer Singh",
"Matt Gardner"
] | Building general reading comprehension systems, capable of solving multiple datasets at the same time, is a recent aspirational goal in the research community. Prior work has focused on model architecture or generalization to held out datasets, and largely passed over the particulars of the multi-task learning set up. ... | 2020.acl-main.86 | 10.18653/v1/2020.acl-main.86 | null | null | null |
2020.acl-main.87 | Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension | https://aclanthology.org/2020.acl-main.87/ | [
"Fei Yuan",
"Linjun Shou",
"Xuanyu Bai",
"Ming Gong",
"Yaobo Liang",
"Nan Duan",
"Yan Fu",
"Daxin Jiang"
] | Multilingual pre-trained models could leverage the training data from a rich source language (such as English) to improve performance on low resource languages. However, the transfer quality for multilingual Machine Reading Comprehension (MRC) is significantly worse than sentence classification tasks mainly due to the ... | 2020.acl-main.87 | 10.18653/v1/2020.acl-main.87 | null | 2004.14069 | title_snapshot |
2020.acl-main.88 | Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading | https://aclanthology.org/2020.acl-main.88/ | [
"Yifan Gao",
"Chien-Sheng Wu",
"Shafiq Joty",
"Caiming Xiong",
"Richard Socher",
"Irwin King",
"Michael Lyu",
"Steven C.H. Hoi"
] | The goal of conversational machine reading is to answer user questions given a knowledge base text which may require asking clarification questions. Existing approaches are limited in their decision making due to struggles in extracting question-related rules and reasoning about them. In this paper, we present a new fr... | 2020.acl-main.88 | 10.18653/v1/2020.acl-main.88 | null | 2005.12484 | title_snapshot |
2020.acl-main.89 | Injecting Numerical Reasoning Skills into Language Models | https://aclanthology.org/2020.acl-main.89/ | [
"Mor Geva",
"Ankit Gupta",
"Jonathan Berant"
] | Large pre-trained language models (LMs) are known to encode substantial amounts of linguistic information. However, high-level reasoning skills, such as numerical reasoning, are difficult to learn from a language-modeling objective only. Consequently, existing models for numerical reasoning have used specialized archit... | 2020.acl-main.89 | 10.18653/v1/2020.acl-main.89 | null | 2004.04487 | title_snapshot |
2020.acl-main.90 | Learning to Identify Follow-Up Questions in Conversational Question Answering | https://aclanthology.org/2020.acl-main.90/ | [
"Souvik Kundu",
"Qian Lin",
"Hwee Tou Ng"
] | Despite recent progress in conversational question answering, most prior work does not focus on follow-up questions. Practical conversational question answering systems often receive follow-up questions in an ongoing conversation, and it is crucial for a system to be able to determine whether a question is a follow-up ... | 2020.acl-main.90 | 10.18653/v1/2020.acl-main.90 | null | null | null |
2020.acl-main.91 | Query Graph Generation for Answering Multi-hop Complex Questions from Knowledge Bases | https://aclanthology.org/2020.acl-main.91/ | [
"Yunshi Lan",
"Jing Jiang"
] | Previous work on answering complex questions from knowledge bases usually separately addresses two types of complexity: questions with constraints and questions with multiple hops of relations. In this paper, we handle both types of complexity at the same time. Motivated by the observation that early incorporation of c... | 2020.acl-main.91 | 10.18653/v1/2020.acl-main.91 | null | null | null |
2020.acl-main.92 | A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers | https://aclanthology.org/2020.acl-main.92/ | [
"Shen-yun Miao",
"Chao-Chun Liang",
"Keh-Yih Su"
] | We present ASDiv (Academia Sinica Diverse MWP Dataset), a diverse (in terms of both language patterns and problem types) English math word problem (MWP) corpus for evaluating the capability of various MWP solvers. Existing MWP corpora for studying AI progress remain limited either in language usage patterns or in probl... | 2020.acl-main.92 | 10.18653/v1/2020.acl-main.92 | null | 2106.15772 | title_snapshot |
2020.acl-main.93 | Improving Image Captioning Evaluation by Considering Inter References Variance | https://aclanthology.org/2020.acl-main.93/ | [
"Yanzhi Yi",
"Hangyu Deng",
"Jinglu Hu"
] | Evaluating image captions is very challenging partially due to the fact that there are multiple correct captions for every single image. Most of the existing one-to-one metrics operate by penalizing mismatches between reference and generative caption without considering the intrinsic variance between ground truth capti... | 2020.acl-main.93 | 10.18653/v1/2020.acl-main.93 | null | null | null |
2020.acl-main.94 | Revisiting the Context Window for Cross-lingual Word Embeddings | https://aclanthology.org/2020.acl-main.94/ | [
"Ryokan Ri",
"Yoshimasa Tsuruoka"
] | Existing approaches to mapping-based cross-lingual word embeddings are based on the assumption that the source and target embedding spaces are structurally similar. The structures of embedding spaces largely depend on the co-occurrence statistics of each word, which the choice of context window determines. Despite this... | 2020.acl-main.94 | 10.18653/v1/2020.acl-main.94 | null | 2004.10813 | title_snapshot |
2020.acl-main.95 | Moving Down the Long Tail of Word Sense Disambiguation with Gloss Informed Bi-encoders | https://aclanthology.org/2020.acl-main.95/ | [
"Terra Blevins",
"Luke Zettlemoyer"
] | A major obstacle in Word Sense Disambiguation (WSD) is that word senses are not uniformly distributed, causing existing models to generally perform poorly on senses that are either rare or unseen during training. We propose a bi-encoder model that independently embeds (1) the target word with its surrounding context an... | 2020.acl-main.95 | 10.18653/v1/2020.acl-main.95 | null | 2005.02590 | title_judge |
2020.acl-main.96 | Code-Switching Patterns Can Be an Effective Route to Improve Performance of Downstream NLP Applications: A Case Study of Humour, Sarcasm and Hate Speech Detection | https://aclanthology.org/2020.acl-main.96/ | [
"Srijan Bansal",
"Vishal Garimella",
"Ayush Suhane",
"Jasabanta Patro",
"Animesh Mukherjee"
] | In this paper, we demonstrate how code-switching patterns can be utilised to improve various downstream NLP applications. In particular, we encode various switching features to improve humour, sarcasm and hate speech detection tasks. We believe that this simple linguistic observation can also be potentially helpful in ... | 2020.acl-main.96 | 10.18653/v1/2020.acl-main.96 | null | 2005.02295 | title_snapshot |
2020.acl-main.97 | DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification | https://aclanthology.org/2020.acl-main.97/ | [
"Lianwei Wu",
"Yuan Rao",
"Yongqiang Zhao",
"Hao Liang",
"Ambreen Nazir"
] | Recently, many methods discover effective evidence from reliable sources by appropriate neural networks for explainable claim verification, which has been widely recognized. However, in these methods, the discovery process of evidence is nontransparent and unexplained. Simultaneously, the discovered evidence is aimed a... | 2020.acl-main.97 | 10.18653/v1/2020.acl-main.97 | null | 2004.13455 | title_snapshot |
2020.acl-main.98 | Towards Conversational Recommendation over Multi-Type Dialogs | https://aclanthology.org/2020.acl-main.98/ | [
"Zeming Liu",
"Haifeng Wang",
"Zheng-Yu Niu",
"Hua Wu",
"Wanxiang Che",
"Ting Liu"
] | We focus on the study of conversational recommendation in the context of multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user’s interests and feedback. To facilitate the study of this task, w... | 2020.acl-main.98 | 10.18653/v1/2020.acl-main.98 | null | 2005.03954 | title_snapshot |
2020.acl-main.99 | Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification | https://aclanthology.org/2020.acl-main.99/ | [
"Lu Fan",
"Guangfeng Yan",
"Qimai Li",
"Han Liu",
"Xiaotong Zhang",
"Albert Y.S. Lam",
"Xiao-Ming Wu"
] | User intent classification plays a vital role in dialogue systems. Since user intent may frequently change over time in many realistic scenarios, unknown (new) intent detection has become an essential problem, where the study has just begun. This paper proposes a semantic-enhanced Gaussian mixture model (SEG) for unkno... | 2020.acl-main.99 | 10.18653/v1/2020.acl-main.99 | null | null | null |
2020.acl-main.100 | Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen | https://aclanthology.org/2020.acl-main.100/ | [
"Yixin Cao",
"Ruihao Shui",
"Liangming Pan",
"Min-Yen Kan",
"Zhiyuan Liu",
"Tat-Seng Chua"
] | The curse of knowledge can impede communication between experts and laymen. We propose a new task of expertise style transfer and contribute a manually annotated dataset with the goal of alleviating such cognitive biases. Solving this task not only simplifies the professional language, but also improves the accuracy an... | 2020.acl-main.100 | 10.18653/v1/2020.acl-main.100 | null | 2005.00701 | title_snapshot |
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