no code implementations • ACL 2021 • Zixuan Li, Xiaolong Jin, Saiping Guan, Wei Li, Jiafeng Guo, Yuanzhuo Wang, Xueqi Cheng
Specifically, at the clue searching stage, CluSTeR learns a beam search policy via reinforcement learning (RL) to induce multiple clues from historical facts.
1 code implementation • 21 Apr 2021 • Saiping Guan, Xiaolong Jin, Jiafeng Guo, Yuanzhuo Wang, Xueqi Cheng
However, they mainly focus on link prediction on binary relational data, where facts are usually represented as triples in the form of (head entity, relation, tail entity).
1 code implementation • 21 Apr 2021 • Zixuan Li, Xiaolong Jin, Wei Li, Saiping Guan, Jiafeng Guo, HuaWei Shen, Yuanzhuo Wang, Xueqi Cheng
To capture these properties effectively and efficiently, we propose a novel Recurrent Evolution network based on Graph Convolution Network (GCN), called RE-GCN, which learns the evolutional representations of entities and relations at each timestamp by modeling the KG sequence recurrently.
no code implementations • ACL 2020 • Saiping Guan, Xiaolong Jin, Jiafeng Guo, Yuanzhuo Wang, Xue-Qi Cheng
It aims to infer an unknown element in a partial fact consisting of the primary triple coupled with any number of its auxiliary description(s).
no code implementations • EMNLP 2018 • Wei Li, Xinyan Xiao, Yajuan Lyu, Yuanzhuo Wang
Information selection is the most important component in document summarization task.
Ranked #22 on
Abstractive Text Summarization
on CNN / Daily Mail
no code implementations • EMNLP 2018 • Wei Li, Xinyan Xiao, Yajuan Lyu, Yuanzhuo Wang
Recent neural sequence-to-sequence models have shown significant progress on short text summarization.
Ranked #33 on
Abstractive Text Summarization
on CNN / Daily Mail
no code implementations • ACL 2018 • Yue Zhao, Xiaolong Jin, Yuanzhuo Wang, Xue-Qi Cheng
Document-level information is very important for event detection even at sentence level.
no code implementations • 29 Oct 2017 • Denghui Zhang, Pengshan Cai, Yantao Jia, Manling Li, Yuanzhuo Wang, Xue-Qi Cheng
Fine-grained entity typing aims to assign entity mentions in the free text with types arranged in a hierarchical structure.
1 code implementation • 30 Mar 2017 • Denghui Zhang, Manling Li, Yantao Jia, Yuanzhuo Wang, Xue-Qi Cheng
Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces.
Ranked #1 on
Link Prediction
on WN18 (filtered)
no code implementations • 4 Dec 2015 • Yantao Jia, Yuanzhuo Wang, Hailun Lin, Xiaolong Jin, Xue-Qi Cheng
Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space.