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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.</abstract>
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%0 Conference Proceedings
%T Query-based Instance Discrimination Network for Relational Triple Extraction
%A Tan, Zeqi
%A Shen, Yongliang
%A Hu, Xuming
%A Zhang, Wenqi
%A Cheng, Xiaoxia
%A Lu, Weiming
%A Zhuang, Yueting
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F tan-etal-2022-query
%X 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.
%R 10.18653/v1/2022.emnlp-main.523
%U https://aclanthology.org/2022.emnlp-main.523
%U https://doi.org/10.18653/v1/2022.emnlp-main.523
%P 7677-7690

Markdown (Informal)

[Query-based Instance Discrimination Network for Relational Triple Extraction](https://aclanthology.org/2022.emnlp-main.523) (Tan et al., EMNLP 2022)

• Query-based Instance Discrimination Network for Relational Triple Extraction (Tan et al., EMNLP 2022)

ACL

• Zeqi Tan, Yongliang Shen, Xuming Hu, Wenqi Zhang, Xiaoxia Cheng, Weiming Lu, and Yueting Zhuang. 2022. Query-based Instance Discrimination Network for Relational Triple Extraction. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 7677–7690, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.