Query-based Instance Discrimination Network for Relational Triple Extraction

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


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.
Anthology ID:
2022.emnlp-main.523
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7677–7690
Language:
URL:
https://aclanthology.org/2022.emnlp-main.523
DOI:
10.18653/v1/2022.emnlp-main.523
Bibkey:
Cite (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.
Cite (Informal):
Query-based Instance Discrimination Network for Relational Triple Extraction (Tan et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-main.523.pdf