no code implementations • NAACL (BEA) 2022 • Alexander Kwako, Yixin Wan, Jieyu Zhao, Kai-Wei Chang, Li Cai, Mark Hansen
This study addresses the need to examine potential biases of transformer-based models in the context of automated English speech assessment.
no code implementations • 2 Mar 2024 • Li Cai, Xin Mao, Yuhao Zhou, Zhaoguang Long, Changxu Wu, Man Lan
Knowledge graph representation learning aims to learn low-dimensional vector embeddings for entities and relations in a knowledge graph.
no code implementations • 2 Mar 2024 • Li Cai, Xin Mao, Zhihong Wang, Shangqing Zhao, Yuhao Zhou, Changxu Wu, Man Lan
Temporal knowledge graph completion (TKGC) aims to fill in missing facts within a given temporal knowledge graph at a specific time.
Knowledge Graph Completion Temporal Knowledge Graph Completion
1 code implementation • 17 Oct 2023 • Yao Xu, Shizhu He, Cunguang Wang, Li Cai, Kang Liu, Jun Zhao
However, these methods train KG embeddings and neural set operators concurrently on both simple (one-hop) and complex (multi-hop and logical) queries, which causes performance degradation on simple queries and low training efficiency.
1 code implementation • 12 Jul 2023 • Li Cai, Xin Mao, Youshao Xiao, Changxu Wu, Man Lan
Entity alignment (EA) aims to find the equivalent entity pairs between different knowledge graphs (KGs), which is crucial to promote knowledge fusion.
1 code implementation • COLING 2022 • Li Cai, Xin Mao, Meirong Ma, Hao Yuan, Jianchao Zhu, Man Lan
However, we believe that it is not necessary to learn the embeddings of temporal information in KGs since most TKGs have uniform temporal representations.