@inproceedings{xu-etal-2026-md,
title = "{MD}-{RE}: A Multi-Discrimination Framework for Document-Level Relation Extraction with Adaptive Threshold Shifted Loss",
author = "Xu, Huangming and
Zhang, Fu and
Zhang, Lu and
Yang, Zhixuan and
Cheng, Jingwei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1842/",
pages = "39333--39345",
ISBN = "979-8-89176-462-0",
abstract = "Document-level relation extraction (DocRE) aims to identify relations for an entity pair within a document. Existing methods can be broadly classified into two categories: direct encoding of the entire document or enhancement using extracted evidence sentences. However, the former often introduces noise unrelated to relations, while the latter is heavily dependent on the quality of evidence extraction. Moreover, these DocRE models typically use an adaptive threshold to predict all potential relations for an entity pair. As a result, class imbalance in DocRE often leads the model to learn a high threshold for an entity pair, which in turn causes the model to frequently predict that the entity pair has no relation.\par To address these issues, we propose a Multi-Discrimination framework (MD-RE) that does not rely on evidence sentences. MD-RE employs three discriminators with dynamically adjusted thresholds to independently predict relations, and aggregates their outputs via a weighted fusion strategy. Furthermore, we propose an Adaptive Threshold Shifted Loss (ATSL), which encourages a lower threshold to alleviate the high false negative rate resulting from class imbalance. Experiments on three datasets demonstrate that MD-RE achieves new state-of-the-art results. In addition, ATSL significantly improves the performance of various existing DocRE models. Moreover, combining other losses with MD-RE also yields competitive results. Code is available at https://github.com/xhm-code/MD-RE."
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<abstract>Document-level relation extraction (DocRE) aims to identify relations for an entity pair within a document. Existing methods can be broadly classified into two categories: direct encoding of the entire document or enhancement using extracted evidence sentences. However, the former often introduces noise unrelated to relations, while the latter is heavily dependent on the quality of evidence extraction. Moreover, these DocRE models typically use an adaptive threshold to predict all potential relations for an entity pair. As a result, class imbalance in DocRE often leads the model to learn a high threshold for an entity pair, which in turn causes the model to frequently predict that the entity pair has no relation.\par To address these issues, we propose a Multi-Discrimination framework (MD-RE) that does not rely on evidence sentences. MD-RE employs three discriminators with dynamically adjusted thresholds to independently predict relations, and aggregates their outputs via a weighted fusion strategy. Furthermore, we propose an Adaptive Threshold Shifted Loss (ATSL), which encourages a lower threshold to alleviate the high false negative rate resulting from class imbalance. Experiments on three datasets demonstrate that MD-RE achieves new state-of-the-art results. In addition, ATSL significantly improves the performance of various existing DocRE models. Moreover, combining other losses with MD-RE also yields competitive results. Code is available at https://github.com/xhm-code/MD-RE.</abstract>
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%0 Conference Proceedings
%T MD-RE: A Multi-Discrimination Framework for Document-Level Relation Extraction with Adaptive Threshold Shifted Loss
%A Xu, Huangming
%A Zhang, Fu
%A Zhang, Lu
%A Yang, Zhixuan
%A Cheng, Jingwei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F xu-etal-2026-md
%X Document-level relation extraction (DocRE) aims to identify relations for an entity pair within a document. Existing methods can be broadly classified into two categories: direct encoding of the entire document or enhancement using extracted evidence sentences. However, the former often introduces noise unrelated to relations, while the latter is heavily dependent on the quality of evidence extraction. Moreover, these DocRE models typically use an adaptive threshold to predict all potential relations for an entity pair. As a result, class imbalance in DocRE often leads the model to learn a high threshold for an entity pair, which in turn causes the model to frequently predict that the entity pair has no relation.\par To address these issues, we propose a Multi-Discrimination framework (MD-RE) that does not rely on evidence sentences. MD-RE employs three discriminators with dynamically adjusted thresholds to independently predict relations, and aggregates their outputs via a weighted fusion strategy. Furthermore, we propose an Adaptive Threshold Shifted Loss (ATSL), which encourages a lower threshold to alleviate the high false negative rate resulting from class imbalance. Experiments on three datasets demonstrate that MD-RE achieves new state-of-the-art results. In addition, ATSL significantly improves the performance of various existing DocRE models. Moreover, combining other losses with MD-RE also yields competitive results. Code is available at https://github.com/xhm-code/MD-RE.
%U https://aclanthology.org/2026.findings-emnlp.1842/
%P 39333-39345
Markdown (Informal)
[MD-RE: A Multi-Discrimination Framework for Document-Level Relation Extraction with Adaptive Threshold Shifted Loss](https://aclanthology.org/2026.findings-emnlp.1842/) (Xu et al., Findings 2026)
ACL