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    <name type="personal">
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    <name type="personal">
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    <abstract>Event argument extraction (EAE) aims to identify the arguments of an event and classify the roles that those arguments play. Despite great efforts made in prior work, there remain many challenges: (1) Data scarcity. (2) Capturing the long-range dependency, specifically, the connection between an event trigger and a distant event argument. (3) Integrating event trigger information into candidate argument representation. For (1), we explore using unlabeled data. For (2), we use Transformer that uses dependency parses to guide the attention mechanism. For (3), we propose a trigger-aware sequence encoder with several types of trigger-dependent sequence representations. We also support argument extraction either from text annotated with gold entities or from plain text. Experiments on the English ACE 2005 benchmark show that our approach achieves a new state-of-the-art.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.318</identifier>
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%0 Conference Proceedings
%T Resource-Enhanced Neural Model for Event Argument Extraction
%A Ma, Jie
%A Wang, Shuai
%A Anubhai, Rishita
%A Ballesteros, Miguel
%A Al-Onaizan, Yaser
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F ma-etal-2020-resource
%X Event argument extraction (EAE) aims to identify the arguments of an event and classify the roles that those arguments play. Despite great efforts made in prior work, there remain many challenges: (1) Data scarcity. (2) Capturing the long-range dependency, specifically, the connection between an event trigger and a distant event argument. (3) Integrating event trigger information into candidate argument representation. For (1), we explore using unlabeled data. For (2), we use Transformer that uses dependency parses to guide the attention mechanism. For (3), we propose a trigger-aware sequence encoder with several types of trigger-dependent sequence representations. We also support argument extraction either from text annotated with gold entities or from plain text. Experiments on the English ACE 2005 benchmark show that our approach achieves a new state-of-the-art.
%R 10.18653/v1/2020.findings-emnlp.318
%U https://aclanthology.org/2020.findings-emnlp.318
%U https://doi.org/10.18653/v1/2020.findings-emnlp.318
%P 3554-3559

Markdown (Informal)

[Resource-Enhanced Neural Model for Event Argument Extraction](https://aclanthology.org/2020.findings-emnlp.318) (Ma et al., Findings 2020)

• Resource-Enhanced Neural Model for Event Argument Extraction (Ma et al., Findings 2020)

ACL

• Jie Ma, Shuai Wang, Rishita Anubhai, Miguel Ballesteros, and Yaser Al-Onaizan. 2020. Resource-Enhanced Neural Model for Event Argument Extraction. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3554–3559, Online. Association for Computational Linguistics.