82 citations · 205 across the 20 of their papers we have counts for
21 papers
OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding
Hao Peng, Xiaozhi Wang, Feng Yao +5
Event understanding aims at understanding the content and relationship of events within texts, which covers multiple complicated information extraction tasks: event detection, even…
Syntactically Robust Training on Partially-Observed Data for Open Information Extraction
Ji Qi, Yuxiang Chen, Lei Hou +2
Open Information Extraction models have shown promising results with sufficient supervision. However, these models face a fundamental challenge that the syntactic distribution of t…
Finding Skill Neurons in Pre-trained Transformer-based Language Models
Xiaozhi Wang, Kaiyue Wen, Zhengyan Zhang +3
Transformer-based pre-trained language models have demonstrated superior performance on various natural language processing tasks. However, it remains unclear how the skills requir…
MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction
Xiaozhi Wang, Yulin Chen, Ning Ding +9
The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two…
COPEN: Probing Conceptual Knowledge in Pre-trained Language Models
Hao Peng, Xiaozhi Wang, Shengding Hu +5
Conceptual knowledge is fundamental to human cognition and knowledge bases. However, existing knowledge probing works only focus on evaluating factual knowledge of pre-trained lang…
GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation
Lunyiu Nie, Shulin Cao, Jiaxin Shi +5
Subject to the huge semantic gap between natural and formal languages, neural semantic parsing is typically bottlenecked by its complexity of dealing with both input semantics and…
LEVEN: A Large-Scale Chinese Legal Event Detection Dataset
Feng Yao, Chaojun Xiao, Xiaozhi Wang +7
Recognizing facts is the most fundamental step in making judgments, hence detecting events in the legal documents is important to legal case analysis tasks. However, existing Legal…
Interactive Contrastive Learning for Self-supervised Entity Alignment
Kaisheng Zeng, Zhenhao Dong, Lei Hou +6
Self-supervised entity alignment (EA) aims to link equivalent entities across different knowledge graphs (KGs) without seed alignments. The current SOTA self-supervised EA method d…
SQUIRE: A Sequence-to-sequence Framework for Multi-hop Knowledge Graph Reasoning
Yushi Bai, Xin Lv, Juanzi Li +4
Multi-hop knowledge graph (KG) reasoning has been widely studied in recent years to provide interpretable predictions on missing links with evidential paths. Most previous works us…
On Transferability of Prompt Tuning for Natural Language Processing
Yusheng Su, Xiaozhi Wang, Yujia Qin +10
Prompt tuning (PT) is a promising parameter-efficient method to utilize extremely large pre-trained language models (PLMs), which can achieve comparable performance to full-paramet…
Program Transfer for Answering Complex Questions over Knowledge Bases
Shulin Cao, Jiaxin Shi, Zijun Yao +6
Program induction for answering complex questions over knowledge bases (KBs) aims to decompose a question into a multi-step program, whose execution against the KB produces the fin…
TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph
Jiaxin Shi, Shulin Cao, Lei Hou +2
Multi-hop Question Answering (QA) is a challenging task because it requires precise reasoning with entity relations at every step towards the answer. The relations can be represent…
Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph
Xin Lv, Xu Han, Lei Hou +6
Multi-hop reasoning has been widely studied in recent years to seek an effective and interpretable method for knowledge graph (KG) completion. Most previous reasoning methods are d…
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base
Shulin Cao, Jiaxin Shi, Liangming Pan +6
Complex question answering over knowledge base (Complex KBQA) is challenging because it requires various compositional reasoning capabilities, such as multi-hop inference, attribut…
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…
Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision
Yixin Cao, Lei Hou, Juanzi Li +4
Joint representation learning of words and entities benefits many NLP tasks, but has not been well explored in cross-lingual settings. In this paper, we propose a novel method for…
Neural Collective Entity Linking
Yixin Cao, Lei Hou, Juanzi Li +1
Entity Linking aims to link entity mentions in texts to knowledge bases, and neural models have achieved recent success in this task. However, most existing methods rely on local c…
Learning to Embed Sentences Using Attentive Recursive Trees
Jiaxin Shi, Lei Hou, Juanzi Li +2
Sentence embedding is an effective feature representation for most deep learning-based NLP tasks. One prevailing line of methods is using recursive latent tree-structured networks…
Differentiating Concepts and Instances for Knowledge Graph Embedding
Xin Lv, Lei Hou, Juanzi Li +1
Concepts, which represent a group of different instances sharing common properties, are essential information in knowledge representation. Most conventional knowledge embedding met…
DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization
Jiaxin Shi, Chen Liang, Lei Hou +3
We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization. Given any document-summary pair, we estimate a salience scor…