EMNLP
Collection
Accepted papers for EMNLP (Conference on Empirical Methods in Natural Language Processing), one dataset per year. • 13 items • Updated
paper_id stringlengths 8 8 | title stringlengths 30 136 | paper_url stringlengths 34 34 | authors listlengths 1 24 | abstract large_stringlengths 432 1.68k | anthology_id stringlengths 8 8 | doi stringlengths 20 20 | award stringclasses 3
values | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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D19-1001 | Attending to Future Tokens for Bidirectional Sequence Generation | https://aclanthology.org/D19-1001/ | [
"Carolin Lawrence",
"Bhushan Kotnis",
"Mathias Niepert"
] | Neural sequence generation is typically performed token-by-token and left-to-right. Whenever a token is generated only previously produced tokens are taken into consideration. In contrast, for problems such as sequence classification, bidirectional attention, which takes both past and future tokens into consideration, ... | D19-1001 | 10.18653/v1/D19-1001 | null | 1908.05915 | title_snapshot |
D19-1002 | Attention is not not Explanation | https://aclanthology.org/D19-1002/ | [
"Sarah Wiegreffe",
"Yuval Pinter"
] | Attention mechanisms play a central role in NLP systems, especially within recurrent neural network (RNN) models. Recently, there has been increasing interest in whether or not the intermediate representations offered by these modules may be used to explain the reasoning for a model’s prediction, and consequently reach... | D19-1002 | 10.18653/v1/D19-1002 | null | 1908.04626 | title_snapshot |
D19-1003 | Practical Obstacles to Deploying Active Learning | https://aclanthology.org/D19-1003/ | [
"David Lowell",
"Zachary C. Lipton",
"Byron C. Wallace"
] | Active learning (AL) is a widely-used training strategy for maximizing predictive performance subject to a fixed annotation budget. In AL, one iteratively selects training examples for annotation, often those for which the current model is most uncertain (by some measure). The hope is that active sampling leads to bett... | D19-1003 | 10.18653/v1/D19-1003 | null | 1807.04801 | title_snapshot |
D19-1004 | Transfer Learning Between Related Tasks Using Expected Label Proportions | https://aclanthology.org/D19-1004/ | [
"Matan Ben Noach",
"Yoav Goldberg"
] | Deep learning systems thrive on abundance of labeled training data but such data is not always available, calling for alternative methods of supervision. One such method is expectation regularization (XR) (Mann and McCallum, 2007), where models are trained based on expected label proportions. We propose a novel applica... | D19-1004 | 10.18653/v1/D19-1004 | null | 1909.00430 | title_snapshot |
D19-1005 | Knowledge Enhanced Contextual Word Representations | https://aclanthology.org/D19-1005/ | [
"Matthew E. Peters",
"Mark Neumann",
"Robert Logan",
"Roy Schwartz",
"Vidur Joshi",
"Sameer Singh",
"Noah A. Smith"
] | Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember facts about those entities. We propose a general method to embed multiple knowledge bases (KBs) into large scale models, and thereby enhance t... | D19-1005 | 10.18653/v1/D19-1005 | null | 1909.04164 | title_snapshot |
D19-1006 | How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings | https://aclanthology.org/D19-1006/ | [
"Kawin Ethayarajh"
] | Replacing static word embeddings with contextualized word representations has yielded significant improvements on many NLP tasks. However, just how contextual are the contextualized representations produced by models such as ELMo and BERT? Are there infinitely many context-specific representations for each word, or are... | D19-1006 | 10.18653/v1/D19-1006 | null | 1909.00512 | title_snapshot |
D19-1007 | Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings | https://aclanthology.org/D19-1007/ | [
"Philippa Shoemark",
"Farhana Ferdousi Liza",
"Dong Nguyen",
"Scott A. Hale",
"Barbara McGillivray"
] | Word embeddings are increasingly used for the automatic detection of semantic change; yet, a robust evaluation and systematic comparison of the choices involved has been lacking. We propose a new evaluation framework for semantic change detection and find that (i) using the whole time series is preferable over only com... | D19-1007 | 10.18653/v1/D19-1007 | null | null | null |
D19-1008 | Correlations between Word Vector Sets | https://aclanthology.org/D19-1008/ | [
"Vitalii Zhelezniak",
"April Shen",
"Daniel Busbridge",
"Aleksandar Savkov",
"Nils Hammerla"
] | Similarity measures based purely on word embeddings are comfortably competing with much more sophisticated deep learning and expert-engineered systems on unsupervised semantic textual similarity (STS) tasks. In contrast to commonly used geometric approaches, we treat a single word embedding as e.g. 300 observations fro... | D19-1008 | 10.18653/v1/D19-1008 | null | 1910.02902 | title_snapshot |
D19-1009 | Game Theory Meets Embeddings: a Unified Framework for Word Sense Disambiguation | https://aclanthology.org/D19-1009/ | [
"Rocco Tripodi",
"Roberto Navigli"
] | Game-theoretic models, thanks to their intrinsic ability to exploit contextual information, have shown to be particularly suited for the Word Sense Disambiguation task. They represent ambiguous words as the players of a non cooperative game and their senses as the strategies that the players can select in order to play... | D19-1009 | 10.18653/v1/D19-1009 | null | null | null |
D19-1010 | Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog | https://aclanthology.org/D19-1010/ | [
"Ryuichi Takanobu",
"Hanlin Zhu",
"Minlie Huang"
] | Dialog policy decides what and how a task-oriented dialog system will respond, and plays a vital role in delivering effective conversations. Many studies apply Reinforcement Learning to learn a dialog policy with the reward function which requires elaborate design and pre-specified user goals. With the growing needs to... | D19-1010 | 10.18653/v1/D19-1010 | null | 1908.10719 | title_snapshot |
D19-1011 | Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots | https://aclanthology.org/D19-1011/ | [
"Chunyuan Yuan",
"Wei Zhou",
"Mingming Li",
"Shangwen Lv",
"Fuqing Zhu",
"Jizhong Han",
"Songlin Hu"
] | Multi-turn retrieval-based conversation is an important task for building intelligent dialogue systems. Existing works mainly focus on matching candidate responses with every context utterance on multiple levels of granularity, which ignore the side effect of using excessive context information. Context utterances prov... | D19-1011 | 10.18653/v1/D19-1011 | null | null | null |
D19-1012 | MoEL: Mixture of Empathetic Listeners | https://aclanthology.org/D19-1012/ | [
"Zhaojiang Lin",
"Andrea Madotto",
"Jamin Shin",
"Peng Xu",
"Pascale Fung"
] | Previous research on empathetic dialogue systems has mostly focused on generating responses given certain emotions. However, being empathetic not only requires the ability of generating emotional responses, but more importantly, requires the understanding of user emotions and replying appropriately. In this paper, we p... | D19-1012 | 10.18653/v1/D19-1012 | null | 1908.07687 | title_snapshot |
D19-1013 | Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever | https://aclanthology.org/D19-1013/ | [
"Libo Qin",
"Yijia Liu",
"Wanxiang Che",
"Haoyang Wen",
"Yangming Li",
"Ting Liu"
] | Querying the knowledge base (KB) has long been a challenge in the end-to-end task-oriented dialogue system. Previous sequence-to-sequence (Seq2Seq) dialogue generation work treats the KB query as an attention over the entire KB, without the guarantee that the generated entities are consistent with each other. In this p... | D19-1013 | 10.18653/v1/D19-1013 | null | 1909.06762 | title_snapshot |
D19-1014 | Building Task-Oriented Visual Dialog Systems Through Alternative Optimization Between Dialog Policy and Language Generation | https://aclanthology.org/D19-1014/ | [
"Mingyang Zhou",
"Josh Arnold",
"Zhou Yu"
] | Reinforcement learning (RL) is an effective approach to learn an optimal dialog policy for task-oriented visual dialog systems. A common practice is to apply RL on a neural sequence-to-sequence(seq2seq) framework with the action space being the output vocabulary in the decoder. However, it is difficult to design a rewa... | D19-1014 | 10.18653/v1/D19-1014 | null | 1909.05365 | title_snapshot |
D19-1015 | DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation | https://aclanthology.org/D19-1015/ | [
"Deepanway Ghosal",
"Navonil Majumder",
"Soujanya Poria",
"Niyati Chhaya",
"Alexander Gelbukh"
] | Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources. In this paper, we present Dialogue Graph Convolutional Network (DialogueGCN), a graph neural network based a... | D19-1015 | 10.18653/v1/D19-1015 | null | 1908.11540 | title_snapshot |
D19-1016 | Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations | https://aclanthology.org/D19-1016/ | [
"Peixiang Zhong",
"Di Wang",
"Chunyan Miao"
] | Messages in human conversations inherently convey emotions. The task of detecting emotions in textual conversations leads to a wide range of applications such as opinion mining in social networks. However, enabling machines to analyze emotions in conversations is challenging, partly because humans often rely on the con... | D19-1016 | 10.18653/v1/D19-1016 | null | 1909.10681 | title_snapshot |
D19-1017 | Interpretable Relevant Emotion Ranking with Event-Driven Attention | https://aclanthology.org/D19-1017/ | [
"Yang Yang",
"Deyu Zhou",
"Yulan He",
"Meng Zhang"
] | Multiple emotions with different intensities are often evoked by events described in documents. Oftentimes, such event information is hidden and needs to be discovered from texts. Unveiling the hidden event information can help to understand how the emotions are evoked and provide explainable results. However, existing... | D19-1017 | 10.18653/v1/D19-1017 | null | null | null |
D19-1018 | Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects | https://aclanthology.org/D19-1018/ | [
"Jianmo Ni",
"Jiacheng Li",
"Julian McAuley"
] | Several recent works have considered the problem of generating reviews (or ‘tips’) as a form of explanation as to why a recommendation might match a customer’s interests. While promising, we demonstrate that existing approaches struggle (in terms of both quality and content) to generate justifications that are relevant... | D19-1018 | 10.18653/v1/D19-1018 | null | null | null |
D19-1019 | Using Customer Service Dialogues for Satisfaction Analysis with Context-Assisted Multiple Instance Learning | https://aclanthology.org/D19-1019/ | [
"Kaisong Song",
"Lidong Bing",
"Wei Gao",
"Jun Lin",
"Lujun Zhao",
"Jiancheng Wang",
"Changlong Sun",
"Xiaozhong Liu",
"Qiong Zhang"
] | Customers ask questions and customer service staffs answer their questions, which is the basic service model via multi-turn customer service (CS) dialogues on E-commerce platforms. Existing studies fail to provide comprehensive service satisfaction analysis, namely satisfaction polarity classification (e.g., well satis... | D19-1019 | 10.18653/v1/D19-1019 | null | null | null |
D19-1020 | Leveraging Dependency Forest for Neural Medical Relation Extraction | https://aclanthology.org/D19-1020/ | [
"Linfeng Song",
"Yue Zhang",
"Daniel Gildea",
"Mo Yu",
"Zhiguo Wang",
"Jinsong Su"
] | Medical relation extraction discovers relations between entity mentions in text, such as research articles. For this task, dependency syntax has been recognized as a crucial source of features. Yet in the medical domain, 1-best parse trees suffer from relatively low accuracies, diminishing their usefulness. We investig... | D19-1020 | 10.18653/v1/D19-1020 | null | 1911.04123 | title_snapshot |
D19-1021 | Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data | https://aclanthology.org/D19-1021/ | [
"Ruidong Wu",
"Yuan Yao",
"Xu Han",
"Ruobing Xie",
"Zhiyuan Liu",
"Fen Lin",
"Leyu Lin",
"Maosong Sun"
] | Open relation extraction (OpenRE) aims to extract relational facts from the open-domain corpus. To this end, it discovers relation patterns between named entities and then clusters those semantically equivalent patterns into a united relation cluster. Most OpenRE methods typically confine themselves to unsupervised par... | D19-1021 | 10.18653/v1/D19-1021 | null | null | null |
D19-1022 | Improving Relation Extraction with Knowledge-attention | https://aclanthology.org/D19-1022/ | [
"Pengfei Li",
"Kezhi Mao",
"Xuefeng Yang",
"Qi Li"
] | While attention mechanisms have been proven to be effective in many NLP tasks, majority of them are data-driven. We propose a novel knowledge-attention encoder which incorporates prior knowledge from external lexical resources into deep neural networks for relation extraction task. Furthermore, we present three effecti... | D19-1022 | 10.18653/v1/D19-1022 | null | 1910.02724 | title_snapshot |
D19-1023 | Jointly Learning Entity and Relation Representations for Entity Alignment | https://aclanthology.org/D19-1023/ | [
"Yuting Wu",
"Xiao Liu",
"Yansong Feng",
"Zheng Wang",
"Dongyan Zhao"
] | Entity alignment is a viable means for integrating heterogeneous knowledge among different knowledge graphs (KGs). Recent developments in the field often take an embedding-based approach to model the structural information of KGs so that entity alignment can be easily performed in the embedding space. However, most exi... | D19-1023 | 10.18653/v1/D19-1023 | null | 1909.09317 | title_snapshot |
D19-1024 | Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion | https://aclanthology.org/D19-1024/ | [
"Zihao Wang",
"Kwunping Lai",
"Piji Li",
"Lidong Bing",
"Wai Lam"
] | For large-scale knowledge graphs (KGs), recent research has been focusing on the large proportion of infrequent relations which have been ignored by previous studies. For example few-shot learning paradigm for relations has been investigated. In this work, we further advocate that handling uncommon entities is inevitab... | D19-1024 | 10.18653/v1/D19-1024 | null | 1909.11359 | title_snapshot |
D19-1025 | Low-Resource Name Tagging Learned with Weakly Labeled Data | https://aclanthology.org/D19-1025/ | [
"Yixin Cao",
"Zikun Hu",
"Tat-seng Chua",
"Zhiyuan Liu",
"Heng Ji"
] | Name tagging in low-resource languages or domains suffers from inadequate training data. Existing work heavily relies on additional information, while leaving those noisy annotations unexplored that extensively exist on the web. In this paper, we propose a novel neural model for name tagging solely based on weakly labe... | D19-1025 | 10.18653/v1/D19-1025 | null | 1908.09659 | title_snapshot |
D19-1026 | Learning Dynamic Context Augmentation for Global Entity Linking | https://aclanthology.org/D19-1026/ | [
"Xiyuan Yang",
"Xiaotao Gu",
"Sheng Lin",
"Siliang Tang",
"Yueting Zhuang",
"Fei Wu",
"Zhigang Chen",
"Guoping Hu",
"Xiang Ren"
] | Despite of the recent success of collective entity linking (EL) methods, these “global” inference methods may yield sub-optimal results when the “all-mention coherence” assumption breaks, and often suffer from high computational cost at the inference stage, due to the complex search space. In this paper, we propose a s... | D19-1026 | 10.18653/v1/D19-1026 | null | 1909.02117 | title_snapshot |
D19-1027 | Open Event Extraction from Online Text using a Generative Adversarial Network | https://aclanthology.org/D19-1027/ | [
"Rui Wang",
"Deyu Zhou",
"Yulan He"
] | To extract the structured representations of open-domain events, Bayesian graphical models have made some progress. However, these approaches typically assume that all words in a document are generated from a single event. While this may be true for short text such as tweets, such an assumption does not generally hold ... | D19-1027 | 10.18653/v1/D19-1027 | null | 1908.09246 | title_snapshot |
D19-1028 | Learning to Bootstrap for Entity Set Expansion | https://aclanthology.org/D19-1028/ | [
"Lingyong Yan",
"Xianpei Han",
"Le Sun",
"Ben He"
] | Bootstrapping for Entity Set Expansion (ESE) aims at iteratively acquiring new instances of a specific target category. Traditional bootstrapping methods often suffer from two problems: 1) delayed feedback, i.e., the pattern evaluation relies on both its direct extraction quality and extraction quality in later iterati... | D19-1028 | 10.18653/v1/D19-1028 | null | null | null |
D19-1029 | Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text | https://aclanthology.org/D19-1029/ | [
"Tianwen Jiang",
"Tong Zhao",
"Bing Qin",
"Ting Liu",
"Nitesh Chawla",
"Meng Jiang"
] | Condition is essential in scientific statement. Without the conditions (e.g., equipment, environment) that were precisely specified, facts (e.g., observations) in the statements may no longer be valid. Existing ScienceIE methods, which aim at extracting factual tuples from scientific text, do not consider the condition... | D19-1029 | 10.18653/v1/D19-1029 | null | null | null |
D19-1030 | Cross-lingual Structure Transfer for Relation and Event Extraction | https://aclanthology.org/D19-1030/ | [
"Ananya Subburathinam",
"Di Lu",
"Heng Ji",
"Jonathan May",
"Shih-Fu Chang",
"Avirup Sil",
"Clare Voss"
] | The identification of complex semantic structures such as events and entity relations, already a challenging Information Extraction task, is doubly difficult from sources written in under-resourced and under-annotated languages. We investigate the suitability of cross-lingual structure transfer techniques for these tas... | D19-1030 | 10.18653/v1/D19-1030 | null | null | null |
D19-1031 | Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework | https://aclanthology.org/D19-1031/ | [
"Junfan Chen",
"Richong Zhang",
"Yongyi Mao",
"Hongyu Guo",
"Jie Xu"
] | Distant supervision for relation extraction enables one to effectively acquire structured relations out of very large text corpora with less human efforts. Nevertheless, most of the prior-art models for such tasks assume that the given text can be noisy, but their corresponding labels are clean. Such unrealistic assump... | D19-1031 | 10.18653/v1/D19-1031 | null | 1909.05448 | title_snapshot |
D19-1032 | Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction | https://aclanthology.org/D19-1032/ | [
"Shun Zheng",
"Wei Cao",
"Wei Xu",
"Jiang Bian"
] | Most existing event extraction (EE) methods merely extract event arguments within the sentence scope. However, such sentence-level EE methods struggle to handle soaring amounts of documents from emerging applications, such as finance, legislation, health, etc., where event arguments always scatter across different sent... | D19-1032 | 10.18653/v1/D19-1032 | null | 1904.07535 | title_snapshot |
D19-1033 | Event Detection with Trigger-Aware Lattice Neural Network | https://aclanthology.org/D19-1033/ | [
"Ning Ding",
"Ziran Li",
"Zhiyuan Liu",
"Haitao Zheng",
"Zibo Lin"
] | Event detection (ED) aims to locate trigger words in raw text and then classify them into correct event types. In this task, neural net- work based models became mainstream in re- cent years. However, two problems arise when it comes to languages without natural delim- iters, such as Chinese. First, word-based mod- els... | D19-1033 | 10.18653/v1/D19-1033 | null | null | null |
D19-1034 | A Boundary-aware Neural Model for Nested Named Entity Recognition | https://aclanthology.org/D19-1034/ | [
"Changmeng Zheng",
"Yi Cai",
"Jingyun Xu",
"Ho-fung Leung",
"Guandong Xu"
] | In natural language processing, it is common that many entities contain other entities inside them. Most existing works on named entity recognition (NER) only deal with flat entities but ignore nested ones. We propose a boundary-aware neural model for nested NER which leverages entity boundaries to predict entity categ... | D19-1034 | 10.18653/v1/D19-1034 | null | null | null |
D19-1035 | Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning | https://aclanthology.org/D19-1035/ | [
"Xiangrong Zeng",
"Shizhu He",
"Daojian Zeng",
"Kang Liu",
"Shengping Liu",
"Jun Zhao"
] | The multiple relation extraction task tries to extract all relational facts from a sentence. Existing works didn’t consider the extraction order of relational facts in a sentence. In this paper we argue that the extraction order is important in this task. To take the extraction order into consideration, we apply the re... | D19-1035 | 10.18653/v1/D19-1035 | null | null | null |
D19-1036 | CaRe: Open Knowledge Graph Embeddings | https://aclanthology.org/D19-1036/ | [
"Swapnil Gupta",
"Sreyash Kenkre",
"Partha Talukdar"
] | Open Information Extraction (OpenIE) methods are effective at extracting (noun phrase, relation phrase, noun phrase) triples from text, e.g., (Barack Obama, took birth in, Honolulu). Organization of such triples in the form of a graph with noun phrases (NPs) as nodes and relation phrases (RPs) as edges results in the c... | D19-1036 | 10.18653/v1/D19-1036 | null | null | null |
D19-1037 | Self-Attention Enhanced CNNs and Collaborative Curriculum Learning for Distantly Supervised Relation Extraction | https://aclanthology.org/D19-1037/ | [
"Yuyun Huang",
"Jinhua Du"
] | Distance supervision is widely used in relation extraction tasks, particularly when large-scale manual annotations are virtually impossible to conduct. Although Distantly Supervised Relation Extraction (DSRE) benefits from automatic labelling, it suffers from serious mislabelling issues, i.e. some or all of the instanc... | D19-1037 | 10.18653/v1/D19-1037 | null | null | null |
D19-1038 | Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping | https://aclanthology.org/D19-1038/ | [
"Jian Ni",
"Radu Florian"
] | Relation extraction (RE) seeks to detect and classify semantic relationships between entities, which provides useful information for many NLP applications. Since the state-of-the-art RE models require large amounts of manually annotated data and language-specific resources to achieve high accuracy, it is very challengi... | D19-1038 | 10.18653/v1/D19-1038 | null | 1911.00069 | title_snapshot |
D19-1039 | Leveraging 2-hop Distant Supervision from Table Entity Pairs for Relation Extraction | https://aclanthology.org/D19-1039/ | [
"Xiang Deng",
"Huan Sun"
] | Distant supervision (DS) has been widely used to automatically construct (noisy) labeled data for relation extraction (RE). Given two entities, distant supervision exploits sentences that directly mention them for predicting their semantic relation. We refer to this strategy as 1-hop DS, which unfortunately may not wor... | D19-1039 | 10.18653/v1/D19-1039 | null | 1909.06007 | title_snapshot |
D19-1040 | EntEval: A Holistic Evaluation Benchmark for Entity Representations | https://aclanthology.org/D19-1040/ | [
"Mingda Chen",
"Zewei Chu",
"Yang Chen",
"Karl Stratos",
"Kevin Gimpel"
] | Rich entity representations are useful for a wide class of problems involving entities. Despite their importance, there is no standardized benchmark that evaluates the overall quality of entity representations. In this work, we propose EntEval: a test suite of diverse tasks that require nontrivial understanding of enti... | D19-1040 | 10.18653/v1/D19-1040 | null | 1909.00137 | title_snapshot |
D19-1041 | Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction | https://aclanthology.org/D19-1041/ | [
"Rujun Han",
"Qiang Ning",
"Nanyun Peng"
] | We propose a joint event and temporal relation extraction model with shared representation learning and structured prediction. The proposed method has two advantages over existing work. First, it improves event representation by allowing the event and relation modules to share the same contextualized embeddings and neu... | D19-1041 | 10.18653/v1/D19-1041 | null | 1909.05360 | title_snapshot |
D19-1042 | Hierarchical Text Classification with Reinforced Label Assignment | https://aclanthology.org/D19-1042/ | [
"Yuning Mao",
"Jingjing Tian",
"Jiawei Han",
"Xiang Ren"
] | While existing hierarchical text classification (HTC) methods attempt to capture label hierarchies for model training, they either make local decisions regarding each label or completely ignore the hierarchy information during inference. To solve the mismatch between training and inference as well as modeling label dep... | D19-1042 | 10.18653/v1/D19-1042 | null | 1908.10419 | title_snapshot |
D19-1043 | Investigating Capsule Network and Semantic Feature on Hyperplanes for Text Classification | https://aclanthology.org/D19-1043/ | [
"Chunning Du",
"Haifeng Sun",
"Jingyu Wang",
"Qi Qi",
"Jianxin Liao",
"Chun Wang",
"Bing Ma"
] | As an essential component of natural language processing, text classification relies on deep learning in recent years. Various neural networks are designed for text classification on the basis of word embedding. However, polysemy is a fundamental feature of the natural language, which brings challenges to text classifi... | D19-1043 | 10.18653/v1/D19-1043 | null | null | null |
D19-1044 | Label-Specific Document Representation for Multi-Label Text Classification | https://aclanthology.org/D19-1044/ | [
"Lin Xiao",
"Xin Huang",
"Boli Chen",
"Liping Jing"
] | Multi-label text classification (MLTC) aims to tag most relevant labels for the given document. In this paper, we propose a Label-Specific Attention Network (LSAN) to learn a label-specific document representation. LSAN takes advantage of label semantic information to determine the semantic connection between labels an... | D19-1044 | 10.18653/v1/D19-1044 | null | null | null |
D19-1045 | Hierarchical Attention Prototypical Networks for Few-Shot Text Classification | https://aclanthology.org/D19-1045/ | [
"Shengli Sun",
"Qingfeng Sun",
"Kevin Zhou",
"Tengchao Lv"
] | Most of the current effective methods for text classification tasks are based on large-scale labeled data and a great number of parameters, but when the supervised training data are few and difficult to be collected, these models are not available. In this work, we propose a hierarchical attention prototypical networks... | D19-1045 | 10.18653/v1/D19-1045 | null | null | null |
D19-1046 | Many Faces of Feature Importance: Comparing Built-in and Post-hoc Feature Importance in Text Classification | https://aclanthology.org/D19-1046/ | [
"Vivian Lai",
"Zheng Cai",
"Chenhao Tan"
] | Feature importance is commonly used to explain machine predictions. While feature importance can be derived from a machine learning model with a variety of methods, the consistency of feature importance via different methods remains understudied. In this work, we systematically compare feature importance from built-in ... | D19-1046 | 10.18653/v1/D19-1046 | null | 1910.08534 | title_snapshot |
D19-1047 | Enhancing Local Feature Extraction with Global Representation for Neural Text Classification | https://aclanthology.org/D19-1047/ | [
"Guocheng Niu",
"Hengru Xu",
"Bolei He",
"Xinyan Xiao",
"Hua Wu",
"Sheng Gao"
] | For text classification, traditional local feature driven models learn long dependency by deeply stacking or hybrid modeling. This paper proposes a novel Encoder1-Encoder2 architecture, where global information is incorporated into the procedure of local feature extraction from scratch. In particular, Encoder1 serves a... | D19-1047 | 10.18653/v1/D19-1047 | null | null | null |
D19-1048 | Latent-Variable Generative Models for Data-Efficient Text Classification | https://aclanthology.org/D19-1048/ | [
"Xiaoan Ding",
"Kevin Gimpel"
] | Generative classifiers offer potential advantages over their discriminative counterparts, namely in the areas of data efficiency, robustness to data shift and adversarial examples, and zero-shot learning (Ng and Jordan,2002; Yogatama et al., 2017; Lewis and Fan,2019). In this paper, we improve generative text classifie... | D19-1048 | 10.18653/v1/D19-1048 | null | 1910.00382 | title_snapshot |
D19-1049 | PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space | https://aclanthology.org/D19-1049/ | [
"Omer Anjum",
"Hongyu Gong",
"Suma Bhat",
"Wen-Mei Hwu",
"JinJun Xiong"
] | Finding the right reviewers to assess the quality of conference submissions is a time consuming process for conference organizers. Given the importance of this step, various automated reviewer-paper matching solutions have been proposed to alleviate the burden. Prior approaches including bag-of-words model and probabil... | D19-1049 | 10.18653/v1/D19-1049 | null | 1909.11258 | title_snapshot |
D19-1050 | Linking artificial and human neural representations of language | https://aclanthology.org/D19-1050/ | [
"Jon Gauthier",
"Roger Levy"
] | What information from an act of sentence understanding is robustly represented in the human brain? We investigate this question by comparing sentence encoding models on a brain decoding task, where the sentence that an experimental participant has seen must be predicted from the fMRI signal evoked by the sentence. We t... | D19-1050 | 10.18653/v1/D19-1050 | null | 1910.01244 | title_snapshot |
D19-1051 | Neural Text Summarization: A Critical Evaluation | https://aclanthology.org/D19-1051/ | [
"Wojciech Kryscinski",
"Nitish Shirish Keskar",
"Bryan McCann",
"Caiming Xiong",
"Richard Socher"
] | Text summarization aims at compressing long documents into a shorter form that conveys the most important parts of the original document. Despite increased interest in the community and notable research effort, progress on benchmark datasets has stagnated. We critically evaluate key ingredients of the current research ... | D19-1051 | 10.18653/v1/D19-1051 | null | 1908.08960 | title_snapshot |
D19-1052 | Neural data-to-text generation: A comparison between pipeline and end-to-end architectures | https://aclanthology.org/D19-1052/ | [
"Thiago Castro Ferreira",
"Chris van der Lee",
"Emiel van Miltenburg",
"Emiel Krahmer"
] | Traditionally, most data-to-text applications have been designed using a modular pipeline architecture, in which non-linguistic input data is converted into natural language through several intermediate transformations. By contrast, recent neural models for data-to-text generation have been proposed as end-to-end appro... | D19-1052 | 10.18653/v1/D19-1052 | null | 1908.09022 | title_snapshot |
D19-1053 | MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance | https://aclanthology.org/D19-1053/ | [
"Wei Zhao",
"Maxime Peyrard",
"Fei Liu",
"Yang Gao",
"Christian M. Meyer",
"Steffen Eger"
] | A robust evaluation metric has a profound impact on the development of text generation systems. A desirable metric compares system output against references based on their semantics rather than surface forms. In this paper we investigate strategies to encode system and reference texts to devise a metric that shows a hi... | D19-1053 | 10.18653/v1/D19-1053 | null | 1909.02622 | title_snapshot |
D19-1054 | Select and Attend: Towards Controllable Content Selection in Text Generation | https://aclanthology.org/D19-1054/ | [
"Xiaoyu Shen",
"Jun Suzuki",
"Kentaro Inui",
"Hui Su",
"Dietrich Klakow",
"Satoshi Sekine"
] | Many text generation tasks naturally contain two steps: content selection and surface realization. Current neural encoder-decoder models conflate both steps into a black-box architecture. As a result, the content to be described in the text cannot be explicitly controlled. This paper tackles this problem by decoupling ... | D19-1054 | 10.18653/v1/D19-1054 | null | 1909.04453 | title_snapshot |
D19-1055 | Sentence-Level Content Planning and Style Specification for Neural Text Generation | https://aclanthology.org/D19-1055/ | [
"Xinyu Hua",
"Lu Wang"
] | Building effective text generation systems requires three critical components: content selection, text planning, and surface realization, and traditionally they are tackled as separate problems. Recent all-in-one style neural generation models have made impressive progress, yet they often produce outputs that are incoh... | D19-1055 | 10.18653/v1/D19-1055 | null | 1909.00734 | title_snapshot |
D19-1056 | Translate and Label! An Encoder-Decoder Approach for Cross-lingual Semantic Role Labeling | https://aclanthology.org/D19-1056/ | [
"Angel Daza",
"Anette Frank"
] | We propose a Cross-lingual Encoder-Decoder model that simultaneously translates and generates sentences with Semantic Role Labeling annotations in a resource-poor target language. Unlike annotation projection techniques, our model does not need parallel data during inference time. Our approach can be applied in monolin... | D19-1056 | 10.18653/v1/D19-1056 | null | 1908.11326 | title_snapshot |
D19-1057 | Syntax-Enhanced Self-Attention-Based Semantic Role Labeling | https://aclanthology.org/D19-1057/ | [
"Yue Zhang",
"Rui Wang",
"Luo Si"
] | As a fundamental NLP task, semantic role labeling (SRL) aims to discover the semantic roles for each predicate within one sentence. This paper investigates how to incorporate syntactic knowledge into the SRL task effectively. We present different approaches of en- coding the syntactic information derived from dependenc... | D19-1057 | 10.18653/v1/D19-1057 | null | 1910.11204 | title_snapshot |
D19-1058 | VerbAtlas: a Novel Large-Scale Verbal Semantic Resource and Its Application to Semantic Role Labeling | https://aclanthology.org/D19-1058/ | [
"Andrea Di Fabio",
"Simone Conia",
"Roberto Navigli"
] | We present VerbAtlas, a new, hand-crafted lexical-semantic resource whose goal is to bring together all verbal synsets from WordNet into semantically-coherent frames. The frames define a common, prototypical argument structure while at the same time providing new concept-specific information. In contrast to PropBank, w... | D19-1058 | 10.18653/v1/D19-1058 | null | null | null |
D19-1059 | Parameter-free Sentence Embedding via Orthogonal Basis | https://aclanthology.org/D19-1059/ | [
"Ziyi Yang",
"Chenguang Zhu",
"Weizhu Chen"
] | We propose a simple and robust non-parameterized approach for building sentence representations. Inspired by the Gram-Schmidt Process in geometric theory, we build an orthogonal basis of the subspace spanned by a word and its surrounding context in a sentence. We model the semantic meaning of a word in a sentence based... | D19-1059 | 10.18653/v1/D19-1059 | null | 1810.00438 | title_snapshot |
D19-1060 | Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations | https://aclanthology.org/D19-1060/ | [
"Mingda Chen",
"Zewei Chu",
"Kevin Gimpel"
] | Prior work on pretrained sentence embeddings and benchmarks focus on the capabilities of stand-alone sentences. We propose DiscoEval, a test suite of tasks to evaluate whether sentence representations include broader context information. We also propose a variety of training objectives that makes use of natural annotat... | D19-1060 | 10.18653/v1/D19-1060 | null | 1909.00142 | title_snapshot |
D19-1061 | Extracting Possessions from Social Media: Images Complement Language | https://aclanthology.org/D19-1061/ | [
"Dhivya Chinnappa",
"Srikala Murugan",
"Eduardo Blanco"
] | This paper describes a new dataset and experiments to determine whether authors of tweets possess the objects they tweet about. We work with 5,000 tweets and show that both humans and neural networks benefit from images in addition to text. We also introduce a simple yet effective strategy to incorporate visual informa... | D19-1061 | 10.18653/v1/D19-1061 | null | null | null |
D19-1062 | Learning to Speak and Act in a Fantasy Text Adventure Game | https://aclanthology.org/D19-1062/ | [
"Jack Urbanek",
"Angela Fan",
"Siddharth Karamcheti",
"Saachi Jain",
"Samuel Humeau",
"Emily Dinan",
"Tim Rocktäschel",
"Douwe Kiela",
"Arthur Szlam",
"Jason Weston"
] | We introduce a large-scale crowdsourced text adventure game as a research platform for studying grounded dialogue. In it, agents can perceive, emote, and act whilst conducting dialogue with other agents. Models and humans can both act as characters within the game. We describe the results of training state-of-the-art g... | D19-1062 | 10.18653/v1/D19-1062 | null | 1903.03094 | title_snapshot |
D19-1063 | Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning | https://aclanthology.org/D19-1063/ | [
"Khanh Nguyen",
"Hal Daumé III"
] | Mobile agents that can leverage help from humans can potentially accomplish more complex tasks than they could entirely on their own. We develop “Help, Anna!” (HANNA), an interactive photo-realistic simulator in which an agent fulfills object-finding tasks by requesting and interpreting natural language-and-vision assi... | D19-1063 | 10.18653/v1/D19-1063 | null | 1909.01871 | title_snapshot |
D19-1064 | Incorporating Visual Semantics into Sentence Representations within a Grounded Space | https://aclanthology.org/D19-1064/ | [
"Patrick Bordes",
"Eloi Zablocki",
"Laure Soulier",
"Benjamin Piwowarski",
"Patrick Gallinari"
] | Language grounding is an active field aiming at enriching textual representations with visual information. Generally, textual and visual elements are embedded in the same representation space, which implicitly assumes a one-to-one correspondence between modalities. This hypothesis does not hold when representing words,... | D19-1064 | 10.18653/v1/D19-1064 | null | 2002.02734 | title_snapshot |
D19-1065 | Neural Naturalist: Generating Fine-Grained Image Comparisons | https://aclanthology.org/D19-1065/ | [
"Maxwell Forbes",
"Christine Kaeser-Chen",
"Piyush Sharma",
"Serge Belongie"
] | We introduce the new Birds-to-Words dataset of 41k sentences describing fine-grained differences between photographs of birds. The language collected is highly detailed, while remaining understandable to the everyday observer (e.g., “heart-shaped face,” “squat body”). Paragraph-length descriptions naturally adapt to va... | D19-1065 | 10.18653/v1/D19-1065 | null | 1909.04101 | title_snapshot |
D19-1066 | Fine-Grained Evaluation for Entity Linking | https://aclanthology.org/D19-1066/ | [
"Henry Rosales-Méndez",
"Aidan Hogan",
"Barbara Poblete"
] | The Entity Linking (EL) task identifies entity mentions in a text corpus and associates them with an unambiguous identifier in a Knowledge Base. While much work has been done on the topic, we first present the results of a survey that reveal a lack of consensus in the community regarding what forms of mentions in a tex... | D19-1066 | 10.18653/v1/D19-1066 | null | null | null |
D19-1067 | Supervising Unsupervised Open Information Extraction Models | https://aclanthology.org/D19-1067/ | [
"Arpita Roy",
"Youngja Park",
"Taesung Lee",
"Shimei Pan"
] | We propose a novel supervised open information extraction (Open IE) framework that leverages an ensemble of unsupervised Open IE systems and a small amount of labeled data to improve system performance. It uses the outputs of multiple unsupervised Open IE systems plus a diverse set of lexical and syntactic information ... | D19-1067 | 10.18653/v1/D19-1067 | null | null | null |
D19-1068 | Neural Cross-Lingual Event Detection with Minimal Parallel Resources | https://aclanthology.org/D19-1068/ | [
"Jian Liu",
"Yubo Chen",
"Kang Liu",
"Jun Zhao"
] | The scarcity in annotated data poses a great challenge for event detection (ED). Cross-lingual ED aims to tackle this challenge by transferring knowledge between different languages to boost performance. However, previous cross-lingual methods for ED demonstrated a heavy dependency on parallel resources, which might li... | D19-1068 | 10.18653/v1/D19-1068 | null | null | null |
D19-1069 | KnowledgeNet: A Benchmark Dataset for Knowledge Base Population | https://aclanthology.org/D19-1069/ | [
"Filipe Mesquita",
"Matteo Cannaviccio",
"Jordan Schmidek",
"Paramita Mirza",
"Denilson Barbosa"
] | KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts expressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus enabling the holistic end-to-end evaluation of knowledge base population systems as a whol... | D19-1069 | 10.18653/v1/D19-1069 | null | null | null |
D19-1070 | Effective Use of Transformer Networks for Entity Tracking | https://aclanthology.org/D19-1070/ | [
"Aditya Gupta",
"Greg Durrett"
] | Tracking entities in procedural language requires understanding the transformations arising from actions on entities as well as those entities’ interactions. While self-attention-based pre-trained language encoders like GPT and BERT have been successfully applied across a range of natural language understanding tasks, ... | D19-1070 | 10.18653/v1/D19-1070 | null | 1909.02635 | title_snapshot |
D19-1071 | Explicit Cross-lingual Pre-training for Unsupervised Machine Translation | https://aclanthology.org/D19-1071/ | [
"Shuo Ren",
"Yu Wu",
"Shujie Liu",
"Ming Zhou",
"Shuai Ma"
] | Pre-training has proven to be effective in unsupervised machine translation due to its ability to model deep context information in cross-lingual scenarios. However, the cross-lingual information obtained from shared BPE spaces is inexplicit and limited. In this paper, we propose a novel cross-lingual pre-training meth... | D19-1071 | 10.18653/v1/D19-1071 | null | 1909.00180 | title_snapshot |
D19-1072 | Latent Part-of-Speech Sequences for Neural Machine Translation | https://aclanthology.org/D19-1072/ | [
"Xuewen Yang",
"Yingru Liu",
"Dongliang Xie",
"Xin Wang",
"Niranjan Balasubramanian"
] | Learning target side syntactic structure has been shown to improve Neural Machine Translation (NMT). However, incorporating syntax through latent variables introduces additional complexity in inference, as the models need to marginalize over the latent syntactic structures. To avoid this, models often resort to greedy ... | D19-1072 | 10.18653/v1/D19-1072 | null | 1908.11782 | title_snapshot |
D19-1073 | Improving Back-Translation with Uncertainty-based Confidence Estimation | https://aclanthology.org/D19-1073/ | [
"Shuo Wang",
"Yang Liu",
"Chao Wang",
"Huanbo Luan",
"Maosong Sun"
] | While back-translation is simple and effective in exploiting abundant monolingual corpora to improve low-resource neural machine translation (NMT), the synthetic bilingual corpora generated by NMT models trained on limited authentic bilingual data are inevitably noisy. In this work, we propose to quantify the confidenc... | D19-1073 | 10.18653/v1/D19-1073 | null | 1909.00157 | title_snapshot |
D19-1074 | Towards Linear Time Neural Machine Translation with Capsule Networks | https://aclanthology.org/D19-1074/ | [
"Mingxuan Wang",
"Jun Xie",
"Zhixing Tan",
"Jinsong Su",
"Deyi Xiong",
"Lei Li"
] | In this study, we first investigate a novel capsule network with dynamic routing for linear time Neural Machine Translation (NMT), referred as CapsNMT. CapsNMT uses an aggregation mechanism to map the source sentence into a matrix with pre-determined size, and then applys a deep LSTM network to decode the target sequen... | D19-1074 | 10.18653/v1/D19-1074 | null | 1811.00287 | title_snapshot |
D19-1075 | Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment | https://aclanthology.org/D19-1075/ | [
"Xiaofei Shi",
"Yanghua Xiao"
] | Entity alignment aims to find entities in different knowledge graphs (KGs) that refer to the same real-world object. An effective solution for cross-lingual entity alignment is crucial for many cross-lingual AI and NLP applications. Recently many embedding-based approaches were proposed for cross-lingual entity alignme... | D19-1075 | 10.18653/v1/D19-1075 | null | null | null |
D19-1076 | Supervised and Nonlinear Alignment of Two Embedding Spaces for Dictionary Induction in Low Resourced Languages | https://aclanthology.org/D19-1076/ | [
"Masud Moshtaghi"
] | Enabling cross-lingual NLP tasks by leveraging multilingual word embedding has recently attracted much attention. An important motivation is to support lower resourced languages, however, most efforts focus on demonstrating the effectiveness of the techniques using embeddings derived from similar languages to English w... | D19-1076 | 10.18653/v1/D19-1076 | null | null | null |
D19-1077 | Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT | https://aclanthology.org/D19-1077/ | [
"Shijie Wu",
"Mark Dredze"
] | Pretrained contextual representation models (Peters et al., 2018; Devlin et al., 2018) have pushed forward the state-of-the-art on many NLP tasks. A new release of BERT (Devlin, 2018) includes a model simultaneously pretrained on 104 languages with impressive performance for zero-shot cross-lingual transfer on a natura... | D19-1077 | 10.18653/v1/D19-1077 | null | 1904.09077 | title_snapshot |
D19-1078 | Iterative Dual Domain Adaptation for Neural Machine Translation | https://aclanthology.org/D19-1078/ | [
"Jiali Zeng",
"Yang Liu",
"Jinsong Su",
"Yubing Ge",
"Yaojie Lu",
"Yongjing Yin",
"Jiebo Luo"
] | Previous studies on the domain adaptation for neural machine translation (NMT) mainly focus on the one-pass transferring out-of-domain translation knowledge to in-domain NMT model. In this paper, we argue that such a strategy fails to fully extract the domain-shared translation knowledge, and repeatedly utilizing corpo... | D19-1078 | 10.18653/v1/D19-1078 | null | 1912.07239 | title_snapshot |
D19-1079 | Multi-agent Learning for Neural Machine Translation | https://aclanthology.org/D19-1079/ | [
"Tianchi Bi",
"Hao Xiong",
"Zhongjun He",
"Hua Wu",
"Haifeng Wang"
] | Conventional Neural Machine Translation (NMT) models benefit from the training with an additional agent, e.g., dual learning, and bidirectional decoding with one agent decod- ing from left to right and the other decoding in the opposite direction. In this paper, we extend the training framework to the multi-agent sce- ... | D19-1079 | 10.18653/v1/D19-1079 | null | 1909.01101 | title_snapshot |
D19-1080 | Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages | https://aclanthology.org/D19-1080/ | [
"Yunsu Kim",
"Petre Petrov",
"Pavel Petrushkov",
"Shahram Khadivi",
"Hermann Ney"
] | We present effective pre-training strategies for neural machine translation (NMT) using parallel corpora involving a pivot language, i.e., source-pivot and pivot-target, leading to a significant improvement in source-target translation. We propose three methods to increase the relation among source, pivot, and target l... | D19-1080 | 10.18653/v1/D19-1080 | null | 1909.09524 | title_snapshot |
D19-1081 | Context-Aware Monolingual Repair for Neural Machine Translation | https://aclanthology.org/D19-1081/ | [
"Elena Voita",
"Rico Sennrich",
"Ivan Titov"
] | Modern sentence-level NMT systems often produce plausible translations of isolated sentences. However, when put in context, these translations may end up being inconsistent with each other. We propose a monolingual DocRepair model to correct inconsistencies between sentence-level translations. DocRepair performs automa... | D19-1081 | 10.18653/v1/D19-1081 | null | 1909.01383 | title_snapshot |
D19-1082 | Multi-Granularity Self-Attention for Neural Machine Translation | https://aclanthology.org/D19-1082/ | [
"Jie Hao",
"Xing Wang",
"Shuming Shi",
"Jinfeng Zhang",
"Zhaopeng Tu"
] | Current state-of-the-art neural machine translation (NMT) uses a deep multi-head self-attention network with no explicit phrase information. However, prior work on statistical machine translation has shown that extending the basic translation unit from words to phrases has produced substantial improvements, suggesting ... | D19-1082 | 10.18653/v1/D19-1082 | null | 1909.02222 | title_snapshot |
D19-1083 | Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention | https://aclanthology.org/D19-1083/ | [
"Biao Zhang",
"Ivan Titov",
"Rico Sennrich"
] | The general trend in NLP is towards increasing model capacity and performance via deeper neural networks. However, simply stacking more layers of the popular Transformer architecture for machine translation results in poor convergence and high computational overhead. Our empirical analysis suggests that convergence is ... | D19-1083 | 10.18653/v1/D19-1083 | null | 1908.11365 | title_snapshot |
D19-1084 | A Discriminative Neural Model for Cross-Lingual Word Alignment | https://aclanthology.org/D19-1084/ | [
"Elias Stengel-Eskin",
"Tzu-ray Su",
"Matt Post",
"Benjamin Van Durme"
] | We introduce a novel discriminative word alignment model, which we integrate into a Transformer-based machine translation model. In experiments based on a small number of labeled examples (∼1.7K–5K sentences) we evaluate its performance intrinsically on both English-Chinese and English-Arabic alignment, where we achiev... | D19-1084 | 10.18653/v1/D19-1084 | null | 1909.00444 | title_snapshot |
D19-1085 | One Model to Learn Both: Zero Pronoun Prediction and Translation | https://aclanthology.org/D19-1085/ | [
"Longyue Wang",
"Zhaopeng Tu",
"Xing Wang",
"Shuming Shi"
] | Zero pronouns (ZPs) are frequently omitted in pro-drop languages, but should be recalled in non-pro-drop languages. This discourse phenomenon poses a significant challenge for machine translation (MT) when translating texts from pro-drop to non-pro-drop languages. In this paper, we propose a unified and discourse-aware... | D19-1085 | 10.18653/v1/D19-1085 | null | 1909.00369 | title_snapshot |
D19-1086 | Dynamic Past and Future for Neural Machine Translation | https://aclanthology.org/D19-1086/ | [
"Zaixiang Zheng",
"Shujian Huang",
"Zhaopeng Tu",
"Xin-Yu Dai",
"Jiajun Chen"
] | Previous studies have shown that neural machine translation (NMT) models can benefit from explicitly modeling translated () and untranslated () source contents as recurrent states (CITATION). However, this less interpretable recurrent process hinders its power to model the dynamic updating of and contents during decodi... | D19-1086 | 10.18653/v1/D19-1086 | null | 1904.09646 | title_snapshot |
D19-1087 | Revisit Automatic Error Detection for Wrong and Missing Translation – A Supervised Approach | https://aclanthology.org/D19-1087/ | [
"Wenqiang Lei",
"Weiwen Xu",
"Ai Ti Aw",
"Yuanxin Xiang",
"Tat Seng Chua"
] | While achieving great fluency, current machine translation (MT) techniques are bottle-necked by adequacy issues. To have a closer study of these issues and accelerate model development, we propose automatic detecting adequacy errors in MT hypothesis for MT model evaluation. To do that, we annotate missing and wrong tra... | D19-1087 | 10.18653/v1/D19-1087 | null | null | null |
D19-1088 | Towards Understanding Neural Machine Translation with Word Importance | https://aclanthology.org/D19-1088/ | [
"Shilin He",
"Zhaopeng Tu",
"Xing Wang",
"Longyue Wang",
"Michael Lyu",
"Shuming Shi"
] | Although neural machine translation (NMT) has advanced the state-of-the-art on various language pairs, the interpretability of NMT remains unsatisfactory. In this work, we propose to address this gap by focusing on understanding the input-output behavior of NMT models. Specifically, we measure the word importance by at... | D19-1088 | 10.18653/v1/D19-1088 | null | 1909.00326 | title_snapshot |
D19-1089 | Multilingual Neural Machine Translation with Language Clustering | https://aclanthology.org/D19-1089/ | [
"Xu Tan",
"Jiale Chen",
"Di He",
"Yingce Xia",
"Tao Qin",
"Tie-Yan Liu"
] | Multilingual neural machine translation (NMT), which translates multiple languages using a single model, is of great practical importance due to its advantages in simplifying the training process, reducing online maintenance costs, and enhancing low-resource and zero-shot translation. Given there are thousands of langu... | D19-1089 | 10.18653/v1/D19-1089 | null | 1908.09324 | title_snapshot |
D19-1090 | Don’t Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon Induction | https://aclanthology.org/D19-1090/ | [
"Paula Czarnowska",
"Sebastian Ruder",
"Edouard Grave",
"Ryan Cotterell",
"Ann Copestake"
] | Human translators routinely have to translate rare inflections of words – due to the Zipfian distribution of words in a language. When translating from Spanish, a good translator would have no problem identifying the proper translation of a statistically rare inflection such as habláramos. Note the lexeme itself, habla... | D19-1090 | 10.18653/v1/D19-1090 | null | 1909.02855 | title_snapshot |
D19-1091 | Pushing the Limits of Low-Resource Morphological Inflection | https://aclanthology.org/D19-1091/ | [
"Antonios Anastasopoulos",
"Graham Neubig"
] | Recent years have seen exceptional strides in the task of automatic morphological inflection generation. However, for a long tail of languages the necessary resources are hard to come by, and state-of-the-art neural methods that work well under higher resource settings perform poorly in the face of a paucity of data. I... | D19-1091 | 10.18653/v1/D19-1091 | null | 1908.05838 | title_snapshot |
D19-1092 | Cross-Lingual Dependency Parsing Using Code-Mixed TreeBank | https://aclanthology.org/D19-1092/ | [
"Meishan Zhang",
"Yue Zhang",
"Guohong Fu"
] | Treebank translation is a promising method for cross-lingual transfer of syntactic dependency knowledge. The basic idea is to map dependency arcs from a source treebank to its target translation according to word alignments. This method, however, can suffer from imperfect alignment between source and target words. To a... | D19-1092 | 10.18653/v1/D19-1092 | null | 1909.02235 | title_snapshot |
D19-1093 | Hierarchical Pointer Net Parsing | https://aclanthology.org/D19-1093/ | [
"Linlin Liu",
"Xiang Lin",
"Shafiq Joty",
"Simeng Han",
"Lidong Bing"
] | Transition-based top-down parsing with pointer networks has achieved state-of-the-art results in multiple parsing tasks, while having a linear time complexity. However, the decoder of these parsers has a sequential structure, which does not yield the most appropriate inductive bias for deriving tree structures. In this... | D19-1093 | 10.18653/v1/D19-1093 | null | 1908.11571 | title_snapshot |
D19-1094 | Semi-Supervised Semantic Role Labeling with Cross-View Training | https://aclanthology.org/D19-1094/ | [
"Rui Cai",
"Mirella Lapata"
] | The successful application of neural networks to a variety of NLP tasks has provided strong impetus to develop end-to-end models for semantic role labeling which forego the need for extensive feature engineering. Recent approaches rely on high-quality annotations which are costly to obtain, and mostly unavailable in lo... | D19-1094 | 10.18653/v1/D19-1094 | null | null | null |
D19-1095 | Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations | https://aclanthology.org/D19-1095/ | [
"Zuyi Bao",
"Rui Huang",
"Chen Li",
"Kenny Zhu"
] | Previous work on cross-lingual sequence labeling tasks either requires parallel data or bridges the two languages through word-by-word matching. Such requirements and assumptions are infeasible for most languages, especially for languages with large linguistic distances, e.g., English and Chinese. In this work, we prop... | D19-1095 | 10.18653/v1/D19-1095 | null | 1910.10893 | title_snapshot |
D19-1096 | A Lexicon-Based Graph Neural Network for Chinese NER | https://aclanthology.org/D19-1096/ | [
"Tao Gui",
"Yicheng Zou",
"Qi Zhang",
"Minlong Peng",
"Jinlan Fu",
"Zhongyu Wei",
"Xuanjing Huang"
] | Recurrent neural networks (RNN) used for Chinese named entity recognition (NER) that sequentially track character and word information have achieved great success. However, the characteristic of chain structure and the lack of global semantics determine that RNN-based models are vulnerable to word ambiguities. In this ... | D19-1096 | 10.18653/v1/D19-1096 | null | null | null |
D19-1097 | CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding | https://aclanthology.org/D19-1097/ | [
"Yijin Liu",
"Fandong Meng",
"Jinchao Zhang",
"Jie Zhou",
"Yufeng Chen",
"Jinan Xu"
] | Spoken Language Understanding (SLU) mainly involves two tasks, intent detection and slot filling, which are generally modeled jointly in existing works. However, most existing models fail to fully utilize cooccurrence relations between slots and intents, which restricts their potential performance. To address this issu... | D19-1097 | 10.18653/v1/D19-1097 | null | 1909.06937 | title_snapshot |
D19-1098 | Tree Transformer: Integrating Tree Structures into Self-Attention | https://aclanthology.org/D19-1098/ | [
"Yaushian Wang",
"Hung-Yi Lee",
"Yun-Nung Chen"
] | Pre-training Transformer from large-scale raw texts and fine-tuning on the desired task have achieved state-of-the-art results on diverse NLP tasks. However, it is unclear what the learned attention captures. The attention computed by attention heads seems not to match human intuitions about hierarchical structures. Th... | D19-1098 | 10.18653/v1/D19-1098 | null | 1909.06639 | title_snapshot |
D19-1099 | Semantic Role Labeling with Iterative Structure Refinement | https://aclanthology.org/D19-1099/ | [
"Chunchuan Lyu",
"Shay B. Cohen",
"Ivan Titov"
] | Modern state-of-the-art Semantic Role Labeling (SRL) methods rely on expressive sentence encoders (e.g., multi-layer LSTMs) but tend to model only local (if any) interactions between individual argument labeling decisions. This contrasts with earlier work and also with the intuition that the labels of individual argume... | D19-1099 | 10.18653/v1/D19-1099 | null | 1909.03285 | title_snapshot |
D19-1100 | Entity Projection via Machine Translation for Cross-Lingual NER | https://aclanthology.org/D19-1100/ | [
"Alankar Jain",
"Bhargavi Paranjape",
"Zachary C. Lipton"
] | Although over 100 languages are supported by strong off-the-shelf machine translation systems, only a subset of them possess large annotated corpora for named entity recognition. Motivated by this fact, we leverage machine translation to improve annotation-projection approaches to cross-lingual named entity recognition... | D19-1100 | 10.18653/v1/D19-1100 | null | 1909.05356 | title_snapshot |
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