@inproceedings{wang-etal-2019-hmeae,
title = "{HMEAE}: Hierarchical Modular Event Argument Extraction",
author = "Wang, Xiaozhi and
Wang, Ziqi and
Han, Xu and
Liu, Zhiyuan and
Li, Juanzi and
Li, Peng and
Sun, Maosong and
Zhou, Jie and
Ren, Xiang",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1584",
doi = "10.18653/v1/D19-1584",
pages = "5777--5783",
abstract = "Existing event extraction methods classify each argument role independently, ignoring the conceptual correlations between different argument roles. In this paper, we propose a Hierarchical Modular Event Argument Extraction (HMEAE) model, to provide effective inductive bias from the concept hierarchy of event argument roles. Specifically, we design a neural module network for each basic unit of the concept hierarchy, and then hierarchically compose relevant unit modules with logical operations into a role-oriented modular network to classify a specific argument role. As many argument roles share the same high-level unit module, their correlation can be utilized to extract specific event arguments better. Experiments on real-world datasets show that HMEAE can effectively leverage useful knowledge from the concept hierarchy and significantly outperform the state-of-the-art baselines. The source code can be obtained from \url{https://github.com/thunlp/HMEAE}.",
}
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<abstract>Existing event extraction methods classify each argument role independently, ignoring the conceptual correlations between different argument roles. In this paper, we propose a Hierarchical Modular Event Argument Extraction (HMEAE) model, to provide effective inductive bias from the concept hierarchy of event argument roles. Specifically, we design a neural module network for each basic unit of the concept hierarchy, and then hierarchically compose relevant unit modules with logical operations into a role-oriented modular network to classify a specific argument role. As many argument roles share the same high-level unit module, their correlation can be utilized to extract specific event arguments better. Experiments on real-world datasets show that HMEAE can effectively leverage useful knowledge from the concept hierarchy and significantly outperform the state-of-the-art baselines. The source code can be obtained from https://github.com/thunlp/HMEAE.</abstract>
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%0 Conference Proceedings
%T HMEAE: Hierarchical Modular Event Argument Extraction
%A Wang, Xiaozhi
%A Wang, Ziqi
%A Han, Xu
%A Liu, Zhiyuan
%A Li, Juanzi
%A Li, Peng
%A Sun, Maosong
%A Zhou, Jie
%A Ren, Xiang
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F wang-etal-2019-hmeae
%X Existing event extraction methods classify each argument role independently, ignoring the conceptual correlations between different argument roles. In this paper, we propose a Hierarchical Modular Event Argument Extraction (HMEAE) model, to provide effective inductive bias from the concept hierarchy of event argument roles. Specifically, we design a neural module network for each basic unit of the concept hierarchy, and then hierarchically compose relevant unit modules with logical operations into a role-oriented modular network to classify a specific argument role. As many argument roles share the same high-level unit module, their correlation can be utilized to extract specific event arguments better. Experiments on real-world datasets show that HMEAE can effectively leverage useful knowledge from the concept hierarchy and significantly outperform the state-of-the-art baselines. The source code can be obtained from https://github.com/thunlp/HMEAE.
%R 10.18653/v1/D19-1584
%U https://aclanthology.org/D19-1584
%U https://doi.org/10.18653/v1/D19-1584
%P 5777-5783
Markdown (Informal)
[HMEAE: Hierarchical Modular Event Argument Extraction](https://aclanthology.org/D19-1584) (Wang et al., EMNLP-IJCNLP 2019)
ACL
- Xiaozhi Wang, Ziqi Wang, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Maosong Sun, Jie Zhou, and Xiang Ren. 2019. HMEAE: Hierarchical Modular Event Argument Extraction. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5777–5783, Hong Kong, China. Association for Computational Linguistics.