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    <abstract>Relation triplet extraction is a fundamental task in natural language processing that aims to identify semantic relationships between entities in text. It is particularly challenging in the zero-shot setting, i.e., zero-shot relation triplet extraction (ZeroRTE), where the relation sets between training and test are disjoint. Existing methods deal with this task by integrating relations into prompts, which may lack sufficient understanding of the unseen relations. To address these limitations, this paper presents a novel Two-Agent Game (TAG) approach to deliberate and debate the semantics of unseen relations. TAG consists of two agents, a generator and an extractor. They iteratively interact in three key steps: attempting, criticizing, and rectifying. This enables the agents to fully debate and understand the unseen relations. Experimental results demonstrate consistent improvement over ALBERT-Large, BART, andGPT3.5, without incurring additional inference costs in all cases. Remarkably, our method outperforms strong baselines by a significant margin, achieving an impressive 6%-16% increase in F1 scores, particularly when dealingwith FewRel with five unseen relations.</abstract>
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%0 Conference Proceedings
%T A Two-Agent Game for Zero-shot Relation Triplet Extraction
%A Xu, Ting
%A Yang, Haiqin
%A Zhao, Fei
%A Wu, Zhen
%A Dai, Xinyu
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F xu-etal-2024-two
%X Relation triplet extraction is a fundamental task in natural language processing that aims to identify semantic relationships between entities in text. It is particularly challenging in the zero-shot setting, i.e., zero-shot relation triplet extraction (ZeroRTE), where the relation sets between training and test are disjoint. Existing methods deal with this task by integrating relations into prompts, which may lack sufficient understanding of the unseen relations. To address these limitations, this paper presents a novel Two-Agent Game (TAG) approach to deliberate and debate the semantics of unseen relations. TAG consists of two agents, a generator and an extractor. They iteratively interact in three key steps: attempting, criticizing, and rectifying. This enables the agents to fully debate and understand the unseen relations. Experimental results demonstrate consistent improvement over ALBERT-Large, BART, andGPT3.5, without incurring additional inference costs in all cases. Remarkably, our method outperforms strong baselines by a significant margin, achieving an impressive 6%-16% increase in F1 scores, particularly when dealingwith FewRel with five unseen relations.
%R 10.18653/v1/2024.findings-acl.446
%U https://aclanthology.org/2024.findings-acl.446
%U https://doi.org/10.18653/v1/2024.findings-acl.446
%P 7510-7527

Markdown (Informal)

[A Two-Agent Game for Zero-shot Relation Triplet Extraction](https://aclanthology.org/2024.findings-acl.446) (Xu et al., Findings 2024)

• A Two-Agent Game for Zero-shot Relation Triplet Extraction (Xu et al., Findings 2024)

ACL

• Ting Xu, Haiqin Yang, Fei Zhao, Zhen Wu, and Xinyu Dai. 2024. A Two-Agent Game for Zero-shot Relation Triplet Extraction. In Findings of the Association for Computational Linguistics: ACL 2024, pages 7510–7527, Bangkok, Thailand. Association for Computational Linguistics.