1.4k citations · 5.4k across the 120 of their papers we have counts for
35 papers · 1 filter
CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension
Xingyi Duan, Baoxin Wang, Ziyue Wang +9
We present a Chinese judicial reading comprehension (CJRC) dataset which contains approximately 10K documents and almost 50K questions with answers. The documents come from judgmen…
Multi-channel Reverse Dictionary Model
Lei Zhang, Fanchao Qi, Zhiyuan Liu +3
A reverse dictionary takes the description of a target word as input and outputs the target word together with other words that match the description. Existing reverse dictionary m…
Towards Building a Multilingual Sememe Knowledge Base: Predicting Sememes for BabelNet Synsets
Fanchao Qi, Liang Chang, Maosong Sun +2
A sememe is defined as the minimum semantic unit of human languages. Sememe knowledge bases (KBs), which contain words annotated with sememes, have been successfully applied to man…
JEC-QA: A Legal-Domain Question Answering Dataset
Haoxi Zhong, Chaojun Xiao, Cunchao Tu +3
We present JEC-QA, the largest question answering dataset in the legal domain, collected from the National Judicial Examination of China. The examination is a comprehensive evaluat…
CAIL2019-SCM: A Dataset of Similar Case Matching in Legal Domain
Chaojun Xiao, Haoxi Zhong, Zhipeng Guo +8
In this paper, we introduce CAIL2019-SCM, Chinese AI and Law 2019 Similar Case Matching dataset. CAIL2019-SCM contains 8,964 triplets of cases published by the Supreme People's Cou…
Multi-Paragraph Reasoning with Knowledge-enhanced Graph Neural Network
Deming Ye, Yankai Lin, Zhenghao Liu +2
Multi-paragraph reasoning is indispensable for open-domain question answering (OpenQA), which receives less attention in the current OpenQA systems. In this work, we propose a know…
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu +4
Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text. In contrast, knowledge embedding (KE) methods can effectively represent the relat…
Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs
Houyu Zhang, Zhenghao Liu, Chenyan Xiong +1
Human conversations naturally evolve around related concepts and scatter to multi-hop concepts. This paper presents a new conversation generation model, ConceptFlow, which leverage…
Adversarial Language Games for Advanced Natural Language Intelligence
Yuan Yao, Haoxi Zhong, Zhengyan Zhang +6
We study the problem of adversarial language games, in which multiple agents with conflicting goals compete with each other via natural language interactions. While adversarial lan…
Learning from Explanations with Neural Execution Tree
Ziqi Wang, Yujia Qin, Wenxuan Zhou +5
While deep neural networks have achieved impressive performance on a range of NLP tasks, these data-hungry models heavily rely on labeled data, which restricts their applications i…
FewRel 2.0: Towards More Challenging Few-Shot Relation Classification
Tianyu Gao, Xu Han, Hao Zhu +4
We present FewRel 2.0, a more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of inst…
NumNet: Machine Reading Comprehension with Numerical Reasoning
Qiu Ran, Yankai Lin, Peng Li +2
Numerical reasoning, such as addition, subtraction, sorting and counting is a critical skill in human's reading comprehension, which has not been well considered in existing machin…
Word-level Textual Adversarial Attacking as Combinatorial Optimization
Yuan Zang, Fanchao Qi, Chenghao Yang +4
Adversarial attacks are carried out to reveal the vulnerability of deep neural networks. Textual adversarial attacking is challenging because text is discrete and a small perturbat…
Fine-grained Fact Verification with Kernel Graph Attention Network
Zhenghao Liu, Chenyan Xiong, Maosong Sun +1
Fact Verification requires fine-grained natural language inference capability that finds subtle clues to identify the syntactical and semantically correct but not well-supported cl…
Improving Sequence Modeling Ability of Recurrent Neural Networks via Sememes
Yujia Qin, Fanchao Qi, Sicong Ouyang +5
Sememes, the minimum semantic units of human languages, have been successfully utilized in various natural language processing applications. However, most existing studies exploit…
OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction
Xu Han, Tianyu Gao, Yuan Yao +3
OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement neural models for relation extraction (RE). Specifically, by implementing typical RE…
Course Concept Expansion in MOOCs with External Knowledge and Interactive Game
Jifan Yu, Chenyu Wang, Gan Luo +4
As Massive Open Online Courses (MOOCs) become increasingly popular, it is promising to automatically provide extracurricular knowledge for MOOC users. Suffering from semantic drift…
Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations
Xin Lv, Yuxian Gu, Xu Han +3
Multi-hop knowledge graph (KG) reasoning is an effective and explainable method for predicting the target entity via reasoning paths in query answering (QA) task. Most previous met…
Explore Entity Embedding Effectiveness in Entity Retrieval
Zhenghao Liu, Chenyan Xiong, Maosong Sun +1
This paper explores entity embedding effectiveness in ad-hoc entity retrieval, which introduces distributed representation of entities into entity retrieval. The knowledge graph co…
Multi-Channel Graph Neural Network for Entity Alignment
Yixin Cao, Zhiyuan Liu, Chengjiang Li +2
Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments. In this paper, we propose a novel Multi-channel Graph Neural Network mod…
Low-Resource Name Tagging Learned with Weakly Labeled Data
Yixin Cao, Zikun Hu, Tat-Seng Chua +2
Name tagging in low-resource languages or domains suffers from inadequate training data. Existing work heavily relies on additional information, while leaving those noisy annotatio…
Neural Document Expansion with User Feedback
Yue Yin, Chenyan Xiong, Cheng Luo +1
This paper presents a neural document expansion approach (NeuDEF) that enriches document representations for neural ranking models. NeuDEF harvests expansion terms from queries whi…
Neural Snowball for Few-Shot Relation Learning
Tianyu Gao, Xu Han, Ruobing Xie +4
Knowledge graphs typically undergo open-ended growth of new relations. This cannot be well handled by relation extraction that focuses on pre-defined relations with sufficient trai…
GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification
Jie Zhou, Xu Han, Cheng Yang +4
Fact verification (FV) is a challenging task which requires to retrieve relevant evidence from plain text and use the evidence to verify given claims. Many claims require to simult…