Xiusheng Huang


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Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning
Xiusheng Huang | Hang Yang | Yubo Chen | Jun Zhao | Kang Liu | Weijian Sun | Zuyu Zhao
Proceedings of the 29th International Conference on Computational Linguistics

Document-level relation extraction aims to recognize relations among multiple entity pairs from a whole piece of article. Recent methods achieve considerable performance but still suffer from two challenges: a) the relational entity pairs are sparse, b) the representation of entity pairs is insufficient. In this paper, we propose Pair-Aware and Entity-Enhanced(PAEE) model to solve the aforementioned two challenges. For the first challenge, we design a Pair-Aware Representation module to predict potential relational entity pairs, which constrains the relation extraction to the predicted entity pairs subset rather than all pairs; For the second, we introduce a Entity-Enhanced Representation module to assemble directional entity pairs and obtain a holistic understanding of the entire document. Experimental results show that our approach can obtain state-of-the-art performance on four benchmark datasets DocRED, DWIE, CDR and GDA.


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Named Entity Recognition via Noise Aware Training Mechanism with Data Filter
Xiusheng Huang | Yubo Chen | Shun Wu | Jun Zhao | Yuantao Xie | Weijian Sun
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021