activity
20192022
most citedMulti-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs

28 citations · 83 across the 6 of their papers we have counts for

collaborators

9 papers

cs.CL2022

Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs

Chao Shang, Guangtao Wang, Peng Qi +1

Question answering over temporal knowledge graphs (KGs) efficiently uses facts contained in a temporal KG, which records entity relations and when they occur in time, to answer nat…

cs.CL20214 cited

Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification

Xiaochen Hou, Peng Qi, Guangtao Wang +4

Recent work on aspect-level sentiment classification has demonstrated the efficacy of incorporating syntactic structures such as dependency trees with graph neural networks(GNN), b…

cs.AI2020

Inductive Learning on Commonsense Knowledge Graph Completion

Bin Wang, Guangtao Wang, Jing Huang +3

Commonsense knowledge graph (CKG) is a special type of knowledge graph (KG), where entities are composed of free-form text. However, most existing CKG completion methods focus on t…

cs.CL20208 cited

Entity and Evidence Guided Relation Extraction for DocRED

Kevin Huang, Guangtao Wang, Tengyu Ma +1

Document-level relation extraction is a challenging task which requires reasoning over multiple sentences in order to predict relations in a document. In this paper, we pro-pose a…

cs.CL2019

Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding

Yun Tang, Jing Huang, Guangtao Wang +2

Translational distance-based knowledge graph embedding has shown progressive improvements on the link prediction task, from TransE to the latest state-of-the-art RotatE. However, N…

cs.CL201919 cited

Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents

Ming Tu, Kevin Huang, Guangtao Wang +3

Interpretable multi-hop reading comprehension (RC) over multiple documents is a challenging problem because it demands reasoning over multiple information sources and explaining th…