TISE: A Tripartite In-context Selection Method for Event Argument Extraction

Yanhe Fu, Yanan Cao, Qingyue Wang, Yi Liu

Abstract
In-context learning enhances the reasoning capabilities of LLMs by providing several examples. A direct yet effective approach to obtain in-context example is to select the top-k examples based on their semantic similarity to the test input. However, when applied to event argument extraction (EAE), this approach exhibits two shortcomings: 1) It may select almost identical examples, thus failing to provide additional event information, and 2) It overlooks event attributes, leading to the selected examples being unrelated to the test event type. In this paper, we introduce three necessary requirements when selecting an in-context example for EAE task: semantic similarity, example diversity and event correlation. And we further propose TISE, which scores examples from these three perspectives and integrates them using Determinantal Point Processes to directly select a set of examples as context. Experimental results on the ACE05 dataset demonstrate the effectiveness of TISE and the necessity of three requirements. Furthermore, we surprisingly observe that TISE can achieve superior performance with fewer examples and can even exceed some supervised methods.
Anthology ID:
2024.naacl-long.101
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1801–1818
Language:
URL:
https://aclanthology.org/2024.naacl-long.101
DOI:
10.18653/v1/2024.naacl-long.101
Bibkey:
Cite (ACL):
Yanhe Fu, Yanan Cao, Qingyue Wang, and Yi Liu. 2024. TISE: A Tripartite In-context Selection Method for Event Argument Extraction. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 1801–1818, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
TISE: A Tripartite In-context Selection Method for Event Argument Extraction (Fu et al., NAACL 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-4/2024.naacl-long.101.pdf
PDF Search