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    <abstract>Joint relation extraction models effectively mitigate the error propagation problem inherently present in pipeline models. Nevertheless, joint models face challenges including high computational complexity, complex network architectures, difficult parameter tuning, and notably, limited interpretability. In contrast, recent advances in pipeline relation extraction models (PURE, PL-Marker) have attracted considerable attention due to their lightweight design and high extraction accuracy. A key advancement is the introduction of a marker mechanism, which enhances relation extraction (RE) process by highlighting entities. However, these models primarily focus on generating correct labels. In doing so, they neglect the label selection process. Moreover, they fail to adequately capture the intricate interactions between entity pairs. To overcome these limitations, we develop a Candidate Label Markers (CLMs) mechanism that prioritizes strategic label selection over simple label generation. Furthermore, we facilitate interactions among diverse relation pairs, enabling the identification of more intricate relational patterns. Experimental results show that we achieve a new SOTA performance. Specifically, based on the same Named Entity Recognition (NER) results as theirs, we improve the SOTA methods by 2.5%, 1.9%, 1.2% in terms of strict F1 scores on SciERC, ACE05 and ACE04.</abstract>
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%0 Conference Proceedings
%T SURE: Mutually Visible Objects and Self-generated Candidate Labels For Relation Extraction
%A Feng, Yuxuan
%A Chen, Qian
%A Wu, Qianyou
%A Guo, Xin
%A Wang, Suge
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F feng-etal-2025-sure
%X Joint relation extraction models effectively mitigate the error propagation problem inherently present in pipeline models. Nevertheless, joint models face challenges including high computational complexity, complex network architectures, difficult parameter tuning, and notably, limited interpretability. In contrast, recent advances in pipeline relation extraction models (PURE, PL-Marker) have attracted considerable attention due to their lightweight design and high extraction accuracy. A key advancement is the introduction of a marker mechanism, which enhances relation extraction (RE) process by highlighting entities. However, these models primarily focus on generating correct labels. In doing so, they neglect the label selection process. Moreover, they fail to adequately capture the intricate interactions between entity pairs. To overcome these limitations, we develop a Candidate Label Markers (CLMs) mechanism that prioritizes strategic label selection over simple label generation. Furthermore, we facilitate interactions among diverse relation pairs, enabling the identification of more intricate relational patterns. Experimental results show that we achieve a new SOTA performance. Specifically, based on the same Named Entity Recognition (NER) results as theirs, we improve the SOTA methods by 2.5%, 1.9%, 1.2% in terms of strict F1 scores on SciERC, ACE05 and ACE04.
%U https://aclanthology.org/2025.coling-main.31/
%P 459-468

Markdown (Informal)

[SURE: Mutually Visible Objects and Self-generated Candidate Labels For Relation Extraction](https://aclanthology.org/2025.coling-main.31/) (Feng et al., COLING 2025)

• SURE: Mutually Visible Objects and Self-generated Candidate Labels For Relation Extraction (Feng et al., COLING 2025)

ACL

• Yuxuan Feng, Qian Chen, Qianyou Wu, Xin Guo, and Suge Wang. 2025. SURE: Mutually Visible Objects and Self-generated Candidate Labels For Relation Extraction. In Proceedings of the 31st International Conference on Computational Linguistics, pages 459–468, Abu Dhabi, UAE. Association for Computational Linguistics.