@inproceedings{lu-etal-2021-text2event,
title = "{T}ext2{E}vent: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction",
author = "Lu, Yaojie and
Lin, Hongyu and
Xu, Jin and
Han, Xianpei and
Tang, Jialong and
Li, Annan and
Sun, Le and
Liao, Meng and
Chen, Shaoyi",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.217",
doi = "10.18653/v1/2021.acl-long.217",
pages = "2795--2806",
abstract = "Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event. Traditional methods usually extract event records by decomposing the complex structure prediction task into multiple subtasks. In this paper, we propose Text2Event, a sequence-to-structure generation paradigm that can directly extract events from the text in an end-to-end manner. Specifically, we design a sequence-to-structure network for unified event extraction, a constrained decoding algorithm for event knowledge injection during inference, and a curriculum learning algorithm for efficient model learning. Experimental results show that, by uniformly modeling all tasks in a single model and universally predicting different labels, our method can achieve competitive performance using only record-level annotations in both supervised learning and transfer learning settings.",
}
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<abstract>Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event. Traditional methods usually extract event records by decomposing the complex structure prediction task into multiple subtasks. In this paper, we propose Text2Event, a sequence-to-structure generation paradigm that can directly extract events from the text in an end-to-end manner. Specifically, we design a sequence-to-structure network for unified event extraction, a constrained decoding algorithm for event knowledge injection during inference, and a curriculum learning algorithm for efficient model learning. Experimental results show that, by uniformly modeling all tasks in a single model and universally predicting different labels, our method can achieve competitive performance using only record-level annotations in both supervised learning and transfer learning settings.</abstract>
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%0 Conference Proceedings
%T Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction
%A Lu, Yaojie
%A Lin, Hongyu
%A Xu, Jin
%A Han, Xianpei
%A Tang, Jialong
%A Li, Annan
%A Sun, Le
%A Liao, Meng
%A Chen, Shaoyi
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F lu-etal-2021-text2event
%X Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event. Traditional methods usually extract event records by decomposing the complex structure prediction task into multiple subtasks. In this paper, we propose Text2Event, a sequence-to-structure generation paradigm that can directly extract events from the text in an end-to-end manner. Specifically, we design a sequence-to-structure network for unified event extraction, a constrained decoding algorithm for event knowledge injection during inference, and a curriculum learning algorithm for efficient model learning. Experimental results show that, by uniformly modeling all tasks in a single model and universally predicting different labels, our method can achieve competitive performance using only record-level annotations in both supervised learning and transfer learning settings.
%R 10.18653/v1/2021.acl-long.217
%U https://aclanthology.org/2021.acl-long.217
%U https://doi.org/10.18653/v1/2021.acl-long.217
%P 2795-2806
Markdown (Informal)
[Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://aclanthology.org/2021.acl-long.217) (Lu et al., ACL-IJCNLP 2021)
ACL
- Yaojie Lu, Hongyu Lin, Jin Xu, Xianpei Han, Jialong Tang, Annan Li, Le Sun, Meng Liao, and Shaoyi Chen. 2021. Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2795–2806, Online. Association for Computational Linguistics.