Explicit Role Interaction Network for Event Argument Extraction
Nan Ding, Chunming Hu, Kai Sun, Samuel Mensah, Richong Zhang
Abstract
Event argument extraction is a challenging subtask of event extraction, aiming to identify and assign roles to arguments under a certain event. Existing methods extract arguments of each role independently, ignoring the relationship between different roles. Such an approach hinders the model from learning explicit interactions between different roles to improve the performance of individual argument extraction. As a solution, we design a neural model that we refer to as the Explicit Role Interaction Network (ERIN) which allows for dynamically capturing the correlations between different argument roles within an event. Extensive experiments on the benchmark dataset ACE2005 demonstrate the superiority of our proposed model to existing approaches.
BibTeX
@inproceedings{ding-etal-2022-explicit,
title = "Explicit Role Interaction Network for Event Argument Extraction",
author = "Ding, Nan and
Hu, Chunming and
Sun, Kai and
Mensah, Samuel and
Zhang, Richong",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.254/",
doi = "10.18653/v1/2022.findings-emnlp.254",
pages = "3475--3485"
}