@inproceedings{d417dfc483934f28be378093200cb2ec,
title = "Improving Cascade Decoding with Syntax-aware Aggregator and Contrastive Learning for Event Extraction",
abstract = "Cascade decoding framework has shown superior performance on event extraction tasks. However, it treats a sentence as a sequence and neglects the potential benefits of the syntactic structure of sentences. In this paper, we improve cascade decoding with a novel module and a self-supervised task. Specifically, we propose a syntax-aware aggregator module to model the syntax of a sentence based on cascade decoding framework such that it captures event dependencies as well as syntactic information. Moreover, we design a type discrimination task to learn better syntactic representations of different event types, which could further boost the performance of event extraction. Experimental results on two widely used event extraction datasets demonstrate that our method could improve the original cascade decoding framework by up to 2.2\% percentage points of F1 score and outperform a number of competitive baseline methods.",
author = "Zeyu Sheng and Yuanyuan Liang and Yunshi Lan",
note = "Publisher Copyright: {\textcopyright} 2023 China National Conference on Computational Linguistics.; 22nd Chinese National Conference on Computational Linguistics, CCL 2023 ; Conference date: 03-08-2023 Through 05-08-2023",
year = "2023",
language = "英语",
series = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics, CCL 2023",
publisher = "Association for Computational Linguistics (ACL)",
pages = "748--760",
editor = "Maosong Sun and Bing Qin and Xipeng Qiu and Jing Jiang and Xianpei Han",
booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics, CCL 2023",
address = "澳大利亚",
}