activity
20172022
most citedKBQA: Learning Question Answering over QA Corpora and Knowledge Bases

203 citations · 607 across the 53 of their papers we have counts for

collaborators

66 papers

cs.LG20222 cited

Complex Hyperbolic Knowledge Graph Embeddings with Fast Fourier Transform

Huiru Xiao, Xin Liu, Yangqiu Song +2

The choice of geometric space for knowledge graph (KG) embeddings can have significant effects on the performance of KG completion tasks. The hyperbolic geometry has been shown to…

cs.CL2022

BEKG: A Built Environment Knowledge Graph

Xiaojun Yang, Haoyu Zhong, Penglin Du +6

Practices in the built environment have become more digitalized with the rapid development of modern design and construction technologies. However, the requirement of practitioners…

cs.CL2022

An Empirical Revisiting of Linguistic Knowledge Fusion in Language Understanding Tasks

Changlong Yu, Tianyi Xiao, Lingpeng Kong +2

Though linguistic knowledge emerges during large-scale language model pretraining, recent work attempt to explicitly incorporate human-defined linguistic priors into task-specific…

cs.CL2022

SubeventWriter: Iterative Sub-event Sequence Generation with Coherence Controller

Zhaowei Wang, Hongming Zhang, Tianqing Fang +3

In this paper, we propose a new task of sub-event generation for an unseen process to evaluate the understanding of the coherence of sub-event actions and objects. To solve the pro…

cs.CL20221 cited

PseudoReasoner: Leveraging Pseudo Labels for Commonsense Knowledge Base Population

Tianqing Fang, Quyet V. Do, Hongming Zhang +3

Commonsense Knowledge Base (CSKB) Population aims at reasoning over unseen entities and assertions on CSKBs, and is an important yet hard commonsense reasoning task. One challenge…

cs.CL20222 cited

MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation

Ying Su, Zihao Wang, Tianqing Fang +3

Commonsense reasoning tasks such as commonsense knowledge graph completion and commonsense question answering require powerful representation learning. In this paper, we propose to…