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        <title>Explore Unsupervised Structures in Pretrained Models for Relation Extraction</title>
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    <name type="personal">
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    <abstract>Syntactic trees have been widely applied in relation extraction (RE). However, since parsing qualities are not stable on different text domains and a pre-defined grammar may not well fit the target relation schema, the introduction of syntactic structures sometimes fails to improve RE performances consistently. In this work, we study RE models with various unsupervised structures mined from pre-trained language models (e.g., BERT). We show that, similar to syntactic trees, unsupervised structures are quite informative for RE task: they are able to obtain competitive (even the best) performance scores on benchmark RE datasets (ACE05, WebNLG, SciERC). We also conduct detailed analyses on their abilities of adapting new RE domains and influence of noise links in those structures. The results suggest that unsupervised structures are reasonable alternatives of commonly used syntactic structures in relation extraction models.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.453</identifier>
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%0 Conference Proceedings
%T Explore Unsupervised Structures in Pretrained Models for Relation Extraction
%A Yang, Xi
%A Ji, Tao
%A Wu, Yuanbin
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F yang-etal-2022-explore
%X Syntactic trees have been widely applied in relation extraction (RE). However, since parsing qualities are not stable on different text domains and a pre-defined grammar may not well fit the target relation schema, the introduction of syntactic structures sometimes fails to improve RE performances consistently. In this work, we study RE models with various unsupervised structures mined from pre-trained language models (e.g., BERT). We show that, similar to syntactic trees, unsupervised structures are quite informative for RE task: they are able to obtain competitive (even the best) performance scores on benchmark RE datasets (ACE05, WebNLG, SciERC). We also conduct detailed analyses on their abilities of adapting new RE domains and influence of noise links in those structures. The results suggest that unsupervised structures are reasonable alternatives of commonly used syntactic structures in relation extraction models.
%R 10.18653/v1/2022.findings-emnlp.453
%U https://aclanthology.org/2022.findings-emnlp.453
%U https://doi.org/10.18653/v1/2022.findings-emnlp.453
%P 6103-6117

Markdown (Informal)

[Explore Unsupervised Structures in Pretrained Models for Relation Extraction](https://aclanthology.org/2022.findings-emnlp.453) (Yang et al., Findings 2022)

• Explore Unsupervised Structures in Pretrained Models for Relation Extraction (Yang et al., Findings 2022)

ACL

• Xi Yang, Tao Ji, and Yuanbin Wu. 2022. Explore Unsupervised Structures in Pretrained Models for Relation Extraction. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6103–6117, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.