@inproceedings{liu-etal-2020-event,
title = "Event Extraction as Machine Reading Comprehension",
author = "Liu, Jian and
Chen, Yubo and
Liu, Kang and
Bi, Wei and
Liu, Xiaojiang",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.128",
doi = "10.18653/v1/2020.emnlp-main.128",
pages = "1641--1651",
abstract = "Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts. Previous methods for EE typically model it as a classification task, which are usually prone to the data scarcity problem. In this paper, we propose a new learning paradigm of EE, by explicitly casting it as a machine reading comprehension problem (MRC). Our approach includes an unsupervised question generation process, which can transfer event schema into a set of natural questions, followed by a BERT-based question-answering process to retrieve answers as EE results. This learning paradigm enables us to strengthen the reasoning process of EE, by introducing sophisticated models in MRC, and relieve the data scarcity problem, by introducing the large-scale datasets in MRC. The empirical results show that: i) our approach attains state-of-the-art performance by considerable margins over previous methods. ii) Our model is excelled in the data-scarce scenario, for example, obtaining 49.8{\%} in F1 for event argument extraction with only 1{\%} data, compared with 2.2{\%} of the previous method. iii) Our model also fits with zero-shot scenarios, achieving 37.0{\%} and 16{\%} in F1 on two datasets without using any EE training data.",
}
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<abstract>Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts. Previous methods for EE typically model it as a classification task, which are usually prone to the data scarcity problem. In this paper, we propose a new learning paradigm of EE, by explicitly casting it as a machine reading comprehension problem (MRC). Our approach includes an unsupervised question generation process, which can transfer event schema into a set of natural questions, followed by a BERT-based question-answering process to retrieve answers as EE results. This learning paradigm enables us to strengthen the reasoning process of EE, by introducing sophisticated models in MRC, and relieve the data scarcity problem, by introducing the large-scale datasets in MRC. The empirical results show that: i) our approach attains state-of-the-art performance by considerable margins over previous methods. ii) Our model is excelled in the data-scarce scenario, for example, obtaining 49.8% in F1 for event argument extraction with only 1% data, compared with 2.2% of the previous method. iii) Our model also fits with zero-shot scenarios, achieving 37.0% and 16% in F1 on two datasets without using any EE training data.</abstract>
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%0 Conference Proceedings
%T Event Extraction as Machine Reading Comprehension
%A Liu, Jian
%A Chen, Yubo
%A Liu, Kang
%A Bi, Wei
%A Liu, Xiaojiang
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F liu-etal-2020-event
%X Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts. Previous methods for EE typically model it as a classification task, which are usually prone to the data scarcity problem. In this paper, we propose a new learning paradigm of EE, by explicitly casting it as a machine reading comprehension problem (MRC). Our approach includes an unsupervised question generation process, which can transfer event schema into a set of natural questions, followed by a BERT-based question-answering process to retrieve answers as EE results. This learning paradigm enables us to strengthen the reasoning process of EE, by introducing sophisticated models in MRC, and relieve the data scarcity problem, by introducing the large-scale datasets in MRC. The empirical results show that: i) our approach attains state-of-the-art performance by considerable margins over previous methods. ii) Our model is excelled in the data-scarce scenario, for example, obtaining 49.8% in F1 for event argument extraction with only 1% data, compared with 2.2% of the previous method. iii) Our model also fits with zero-shot scenarios, achieving 37.0% and 16% in F1 on two datasets without using any EE training data.
%R 10.18653/v1/2020.emnlp-main.128
%U https://aclanthology.org/2020.emnlp-main.128
%U https://doi.org/10.18653/v1/2020.emnlp-main.128
%P 1641-1651
Markdown (Informal)
[Event Extraction as Machine Reading Comprehension](https://aclanthology.org/2020.emnlp-main.128) (Liu et al., EMNLP 2020)
ACL
- Jian Liu, Yubo Chen, Kang Liu, Wei Bi, and Xiaojiang Liu. 2020. Event Extraction as Machine Reading Comprehension. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1641–1651, Online. Association for Computational Linguistics.