Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making
ACL 2021
Zijun Yao, Chengjiang Li, Tiansi Dong, Xin Lv, Jifan Yu, Lei Hou, Juanzi Li, Yichi Zhang and Zelin Dai
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We propose to decouple the representation learning stage and the decision making stage to fully utilize unlabeled data for entity matching task.

Graph Symbiosis Learning
Arxiv Preprint
Liang Zeng, Jin Xu, Zijun Yao, Yanqiao Zhu, Jian Li
[PDF]
We introduce a framework for learning from multiple generated graph views, named graph symbiosis learning (GraphSym). In GraphSym, graph neural networks (GNN) developed in multiple generated graph views can adaptively exchange parameters with each other and fuse information stored in linkage structures and node features.