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        <title>Self-distilled Transitive Instance Weighting for Denoised Distantly Supervised Relation Extraction</title>
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    <abstract>The widespread existence of wrongly labeled instances is a challenge to distantly supervised relation extraction. Most of the previous works are trained in a bag-level setting to alleviate such noise. However, sentence-level training better utilizes the information than bag-level training, as long as combined with effective noise alleviation. In this work, we propose a novel Transitive Instance Weighting mechanism integrated with the self-distilled BERT backbone, utilizing information in the intermediate outputs to generate dynamic instance weights for denoised sentence-level training. By down-weighting wrongly labeled instances and discounting the weights of easy-to-fit ones, our method can effectively tackle wrongly labeled instances and prevent overfitting. Experiments on both held-out and manual datasets indicate that our method achieves state-of-the-art performance and consistent improvements over the baselines.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.13</identifier>
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%0 Conference Proceedings
%T Self-distilled Transitive Instance Weighting for Denoised Distantly Supervised Relation Extraction
%A Lin, Xiangyu
%A Jia, Weijia
%A Gong, Zhiguo
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F lin-etal-2023-self
%X The widespread existence of wrongly labeled instances is a challenge to distantly supervised relation extraction. Most of the previous works are trained in a bag-level setting to alleviate such noise. However, sentence-level training better utilizes the information than bag-level training, as long as combined with effective noise alleviation. In this work, we propose a novel Transitive Instance Weighting mechanism integrated with the self-distilled BERT backbone, utilizing information in the intermediate outputs to generate dynamic instance weights for denoised sentence-level training. By down-weighting wrongly labeled instances and discounting the weights of easy-to-fit ones, our method can effectively tackle wrongly labeled instances and prevent overfitting. Experiments on both held-out and manual datasets indicate that our method achieves state-of-the-art performance and consistent improvements over the baselines.
%R 10.18653/v1/2023.findings-emnlp.13
%U https://aclanthology.org/2023.findings-emnlp.13
%U https://doi.org/10.18653/v1/2023.findings-emnlp.13
%P 168-180

Markdown (Informal)

[Self-distilled Transitive Instance Weighting for Denoised Distantly Supervised Relation Extraction](https://aclanthology.org/2023.findings-emnlp.13) (Lin et al., Findings 2023)

• Self-distilled Transitive Instance Weighting for Denoised Distantly Supervised Relation Extraction (Lin et al., Findings 2023)

ACL

• Xiangyu Lin, Weijia Jia, and Zhiguo Gong. 2023. Self-distilled Transitive Instance Weighting for Denoised Distantly Supervised Relation Extraction. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 168–180, Singapore. Association for Computational Linguistics.