@inproceedings{huang-etal-2023-iteratively,
title = "An Iteratively Parallel Generation Method with the Pre-Filling Strategy for Document-level Event Extraction",
author = "Huang, Guanhua and
Xu, Runxin and
Zeng, Ying and
Chen, Jiaze and
Yang, Zhouwang and
E, Weinan",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.668",
doi = "10.18653/v1/2023.emnlp-main.668",
pages = "10834--10852",
abstract = "In document-level event extraction (DEE) tasks, a document typically contains many event records with multiple event roles. Therefore, accurately extracting all event records is a big challenge since the number of event records is not given. Previous works present the entity-based directed acyclic graph (EDAG) generation methods to autoregressively generate event roles, which requires a given generation order. Meanwhile, parallel methods are proposed to generate all event roles simultaneously, but suffer from the inadequate training which manifests zero accuracies on some event roles. In this paper, we propose an Iteratively Parallel Generation method with the Pre-Filling strategy (IPGPF). Event roles in an event record are generated in parallel to avoid order selection, and the event records are iteratively generated to utilize historical results. Experiments on two public datasets show our IPGPF improves 11.7 F1 than previous parallel models and up to 5.1 F1 than auto-regressive models under the control variable settings. Moreover, our enhanced IPGPF outperforms other entity-enhanced models and achieves new state-of-the-art performance.",
}
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<abstract>In document-level event extraction (DEE) tasks, a document typically contains many event records with multiple event roles. Therefore, accurately extracting all event records is a big challenge since the number of event records is not given. Previous works present the entity-based directed acyclic graph (EDAG) generation methods to autoregressively generate event roles, which requires a given generation order. Meanwhile, parallel methods are proposed to generate all event roles simultaneously, but suffer from the inadequate training which manifests zero accuracies on some event roles. In this paper, we propose an Iteratively Parallel Generation method with the Pre-Filling strategy (IPGPF). Event roles in an event record are generated in parallel to avoid order selection, and the event records are iteratively generated to utilize historical results. Experiments on two public datasets show our IPGPF improves 11.7 F1 than previous parallel models and up to 5.1 F1 than auto-regressive models under the control variable settings. Moreover, our enhanced IPGPF outperforms other entity-enhanced models and achieves new state-of-the-art performance.</abstract>
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%0 Conference Proceedings
%T An Iteratively Parallel Generation Method with the Pre-Filling Strategy for Document-level Event Extraction
%A Huang, Guanhua
%A Xu, Runxin
%A Zeng, Ying
%A Chen, Jiaze
%A Yang, Zhouwang
%A E, Weinan
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F huang-etal-2023-iteratively
%X In document-level event extraction (DEE) tasks, a document typically contains many event records with multiple event roles. Therefore, accurately extracting all event records is a big challenge since the number of event records is not given. Previous works present the entity-based directed acyclic graph (EDAG) generation methods to autoregressively generate event roles, which requires a given generation order. Meanwhile, parallel methods are proposed to generate all event roles simultaneously, but suffer from the inadequate training which manifests zero accuracies on some event roles. In this paper, we propose an Iteratively Parallel Generation method with the Pre-Filling strategy (IPGPF). Event roles in an event record are generated in parallel to avoid order selection, and the event records are iteratively generated to utilize historical results. Experiments on two public datasets show our IPGPF improves 11.7 F1 than previous parallel models and up to 5.1 F1 than auto-regressive models under the control variable settings. Moreover, our enhanced IPGPF outperforms other entity-enhanced models and achieves new state-of-the-art performance.
%R 10.18653/v1/2023.emnlp-main.668
%U https://aclanthology.org/2023.emnlp-main.668
%U https://doi.org/10.18653/v1/2023.emnlp-main.668
%P 10834-10852
Markdown (Informal)
[An Iteratively Parallel Generation Method with the Pre-Filling Strategy for Document-level Event Extraction](https://aclanthology.org/2023.emnlp-main.668) (Huang et al., EMNLP 2023)
ACL