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        <title>Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation</title>
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        <namePart type="given">Qingyu</namePart>
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    <abstract>Document-level Relation Extraction (DocRE) is a more challenging task compared to its sentence-level counterpart. It aims to extract relations from multiple sentences at once. In this paper, we propose a semi-supervised framework for DocRE with three novel components. Firstly, we use an axial attention module for learning the interdependency among entity-pairs, which improves the performance on two-hop relations. Secondly, we propose an adaptive focal loss to tackle the class imbalance problem of DocRE. Lastly, we use knowledge distillation to overcome the differences between human annotated data and distantly supervised data. We conducted experiments on two DocRE datasets. Our model consistently outperforms strong baselines and its performance exceeds the previous SOTA by 1.36 F1 and 1.46 Ign_F1 score on the DocRED leaderboard.</abstract>
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%0 Conference Proceedings
%T Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation
%A Tan, Qingyu
%A He, Ruidan
%A Bing, Lidong
%A Ng, Hwee Tou
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F tan-etal-2022-document
%X Document-level Relation Extraction (DocRE) is a more challenging task compared to its sentence-level counterpart. It aims to extract relations from multiple sentences at once. In this paper, we propose a semi-supervised framework for DocRE with three novel components. Firstly, we use an axial attention module for learning the interdependency among entity-pairs, which improves the performance on two-hop relations. Secondly, we propose an adaptive focal loss to tackle the class imbalance problem of DocRE. Lastly, we use knowledge distillation to overcome the differences between human annotated data and distantly supervised data. We conducted experiments on two DocRE datasets. Our model consistently outperforms strong baselines and its performance exceeds the previous SOTA by 1.36 F1 and 1.46 Ign_F1 score on the DocRED leaderboard.
%R 10.18653/v1/2022.findings-acl.132
%U https://aclanthology.org/2022.findings-acl.132
%U https://doi.org/10.18653/v1/2022.findings-acl.132
%P 1672-1681

Markdown (Informal)

[Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation](https://aclanthology.org/2022.findings-acl.132) (Tan et al., Findings 2022)

• Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation (Tan et al., Findings 2022)

ACL

• Qingyu Tan, Ruidan He, Lidong Bing, and Hwee Tou Ng. 2022. Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation. In Findings of the Association for Computational Linguistics: ACL 2022, pages 1672–1681, Dublin, Ireland. Association for Computational Linguistics.