文献类型：期刊文献

英文题名：CascadePAIE: Reallocating Relevance for Event Roles and Event Text in Event Argument Extraction

作者：Yao, Chunyu[1]; Guo, Yi[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

年份：2025

外文期刊名：ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

收录：EI(收录号：20252718723621)

语种：英文

外文关键词：Signal processing

摘要：Event argument extraction is one of the subtasks in event extraction, and the current mainstream approaches define it as a span extraction task utilizing the concept of prompt learning. However, upon reproducing the current mainstream work, we identified two issues with event argument span extraction: 1) the predicted spans for arguments lack precision and may encompass positions near the ground-truth spans, and 2) different roles identify the same argument span. These issues somewhat impact the model's performance. To address these challenges, we propose CascadePAIE. On the one hand, this method reallocates relevance for event text by mapping the relevance to event representations and the loss function, thereby enhancing event representations and penalizing erroneous predictions near the ground-truth spans. On the other hand, it reallocates attention for event roles, assigning lower attention to subsequent roles for spans already predicted by preceding roles, thereby surpassing the limitations of those roles. We conducted extensive experiments on sentence-level and document-level datasets. The results validate the effectiveness of our approach, with average improvements of 1.63%, 1.84%, and 2.28% on the three datasets in the base version, and even achieving improvements of 2.95%, 2.56% and 3.02% in the best experimental results. ? 2025 IEEE.