@inproceedings{tan-etal-2022-query,
title = "Query-based Instance Discrimination Network for Relational Triple Extraction",
author = "Tan, Zeqi and
Shen, Yongliang and
Hu, Xuming and
Zhang, Wenqi and
Cheng, Xiaoxia and
Lu, Weiming and
Zhuang, Yueting",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.523",
doi = "10.18653/v1/2022.emnlp-main.523",
pages = "7677--7690",
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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<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)
ACL