@inproceedings{zeyu-etal-2023-improving,
title = "Improving Cascade Decoding with Syntax-aware Aggregator and Contrastive Learning for Event Extraction",
author = "Zeyu, Sheng and
Yuanyuan, Liang and
Yunshi, Lan",
editor = "Sun, Maosong and
Qin, Bing and
Qiu, Xipeng and
Jiang, Jing and
Han, Xianpei",
booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics",
month = aug,
year = "2023",
address = "Harbin, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2023.ccl-1.64",
pages = "748--760",
abstract = "{``}Cascade decoding framework has shown superior performance on event extraction tasks. How-ever, it treats a sentence as a sequence and neglects the potential benefits of the syntactic struc-ture 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 syntaxof a sentence based on cascade decoding framework such that it captures event dependencies aswell as syntactic information. Moreover, we design a type discrimination task to learn better syn-tactic representations of different event types, which could further boost the performance of eventextraction. Experimental results on two widely used event extraction datasets demonstrate thatour method could improve the original cascade decoding framework by up to 2.2{\%} percentagepoints of F1 score and outperform a number of competitive baseline methods. Introduction{''}",
language = "English",
}
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<abstract>“Cascade decoding framework has shown superior performance on event extraction tasks. How-ever, it treats a sentence as a sequence and neglects the potential benefits of the syntactic struc-ture 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 syntaxof a sentence based on cascade decoding framework such that it captures event dependencies aswell as syntactic information. Moreover, we design a type discrimination task to learn better syn-tactic representations of different event types, which could further boost the performance of eventextraction. Experimental results on two widely used event extraction datasets demonstrate thatour method could improve the original cascade decoding framework by up to 2.2% percentagepoints of F1 score and outperform a number of competitive baseline methods. Introduction”</abstract>
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%0 Conference Proceedings
%T Improving Cascade Decoding with Syntax-aware Aggregator and Contrastive Learning for Event Extraction
%A Zeyu, Sheng
%A Yuanyuan, Liang
%A Yunshi, Lan
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G English
%F zeyu-etal-2023-improving
%X “Cascade decoding framework has shown superior performance on event extraction tasks. How-ever, it treats a sentence as a sequence and neglects the potential benefits of the syntactic struc-ture 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 syntaxof a sentence based on cascade decoding framework such that it captures event dependencies aswell as syntactic information. Moreover, we design a type discrimination task to learn better syn-tactic representations of different event types, which could further boost the performance of eventextraction. Experimental results on two widely used event extraction datasets demonstrate thatour method could improve the original cascade decoding framework by up to 2.2% percentagepoints of F1 score and outperform a number of competitive baseline methods. Introduction”
%U https://aclanthology.org/2023.ccl-1.64
%P 748-760
Markdown (Informal)
[Improving Cascade Decoding with Syntax-aware Aggregator and Contrastive Learning for Event Extraction](https://aclanthology.org/2023.ccl-1.64) (Zeyu et al., CCL 2023)
ACL