@inproceedings{huang-etal-2020-joint,
title = "Joint Event Extraction with Hierarchical Policy Network",
author = "Huang, Peixin and
Zhao, Xiang and
Takanobu, Ryuichi and
Tan, Zhen and
Xiao, Weidong",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.239",
doi = "10.18653/v1/2020.coling-main.239",
pages = "2653--2664",
abstract = "Most existing work on event extraction (EE) either follows a pipelined manner or uses a joint structure but is pipelined in essence. As a result, these efforts fail to utilize information interactions among event triggers, event arguments, and argument roles, which causes information redundancy. In view of this, we propose to exploit the role information of the arguments in an event and devise a Hierarchical Policy Network (HPNet) to perform joint EE. The whole EE process is fulfilled through a two-level hierarchical structure consisting of two policy networks for event detection and argument detection. The deep information interactions among the subtasks are realized, and it is more natural to deal with multiple events issue. Extensive experiments on ACE2005 and TAC2015 demonstrate the superiority of HPNet, leading to state-of-the-art performance and is more powerful for sentences with multiple events.",
}
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<abstract>Most existing work on event extraction (EE) either follows a pipelined manner or uses a joint structure but is pipelined in essence. As a result, these efforts fail to utilize information interactions among event triggers, event arguments, and argument roles, which causes information redundancy. In view of this, we propose to exploit the role information of the arguments in an event and devise a Hierarchical Policy Network (HPNet) to perform joint EE. The whole EE process is fulfilled through a two-level hierarchical structure consisting of two policy networks for event detection and argument detection. The deep information interactions among the subtasks are realized, and it is more natural to deal with multiple events issue. Extensive experiments on ACE2005 and TAC2015 demonstrate the superiority of HPNet, leading to state-of-the-art performance and is more powerful for sentences with multiple events.</abstract>
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%0 Conference Proceedings
%T Joint Event Extraction with Hierarchical Policy Network
%A Huang, Peixin
%A Zhao, Xiang
%A Takanobu, Ryuichi
%A Tan, Zhen
%A Xiao, Weidong
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F huang-etal-2020-joint
%X Most existing work on event extraction (EE) either follows a pipelined manner or uses a joint structure but is pipelined in essence. As a result, these efforts fail to utilize information interactions among event triggers, event arguments, and argument roles, which causes information redundancy. In view of this, we propose to exploit the role information of the arguments in an event and devise a Hierarchical Policy Network (HPNet) to perform joint EE. The whole EE process is fulfilled through a two-level hierarchical structure consisting of two policy networks for event detection and argument detection. The deep information interactions among the subtasks are realized, and it is more natural to deal with multiple events issue. Extensive experiments on ACE2005 and TAC2015 demonstrate the superiority of HPNet, leading to state-of-the-art performance and is more powerful for sentences with multiple events.
%R 10.18653/v1/2020.coling-main.239
%U https://aclanthology.org/2020.coling-main.239
%U https://doi.org/10.18653/v1/2020.coling-main.239
%P 2653-2664
Markdown (Informal)
[Joint Event Extraction with Hierarchical Policy Network](https://aclanthology.org/2020.coling-main.239) (Huang et al., COLING 2020)
ACL
- Peixin Huang, Xiang Zhao, Ryuichi Takanobu, Zhen Tan, and Weidong Xiao. 2020. Joint Event Extraction with Hierarchical Policy Network. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2653–2664, Barcelona, Spain (Online). International Committee on Computational Linguistics.