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arXiv · 2211.01797

Query-based Instance Discrimination Network for Relational Triple Extraction

Zeqi Tan·Yongliang Shen·Xuming Hu·Wenqi Zhang·Xiaoxia Cheng·Weiming Lu·Yueting Zhuang
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Abstract

Joint entity and relation extraction has been a core task in the field of information extraction. Recent approaches usually consider the extraction of relational triples from a stereoscopic perspective, either learning a relation-specific tagger or separate classifiers for each relation type. However, they still suffer from error propagation, relation redundancy and lack of high-level connections between triples. To address these issues, we propose a novel query-based approach to construct instance-level representations for relational triples. By metric-based comparison between query embeddings and token embeddings, we can extract all types of triples in one step, thus eliminating the error propagation problem. In addition, we learn the instance-level representation of relational triples via contrastive learning. In this way, relational triples can not only enclose rich class-level semantics but also access to high-order global connections. Experimental results show that our proposed method achieves the state of the art on five widely used benchmarks.

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Zeqi Tan, Yongliang Shen, Xuming Hu, Wenqi Zhang, Xiaoxia Cheng, Weiming Lu, Yueting Zhuang. 2022-11-03. Query-based Instance Discrimination Network for Relational Triple Extraction. https://arxiv.org/abs/2211.01797

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