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    <abstract>The argument role in event extraction refers to the relation between an event and an argument participating in it. Despite the great progress in event extraction, existing studies still depend on roles pre-defined by domain experts. These studies expose obvious weakness when extending to emerging event types or new domains without available roles. Therefore, more attention and effort needs to be devoted to automatically customizing argument roles. In this paper, we define this essential but under-explored task: open-vocabulary argument role prediction. The goal of this task is to infer a set of argument roles for a given event type. We propose a novel unsupervised framework, RolePred for this task. Specifically, we formulate the role prediction problem as an in-filling task and construct prompts for a pre-trained language model to generate candidate roles. By extracting and analyzing the candidate arguments, the event-specific roles are further merged and selected. To standardize the research of this task, we collect a new human-annotated event extraction dataset including 143 customized argument roles with rich semantics. On this dataset, RolePred outperforms the existing methods by a large margin.</abstract>
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%0 Conference Proceedings
%T Open-Vocabulary Argument Role Prediction For Event Extraction
%A Jiao, Yizhu
%A Li, Sha
%A Xie, Yiqing
%A Zhong, Ming
%A Ji, Heng
%A Han, Jiawei
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F jiao-etal-2022-open
%X The argument role in event extraction refers to the relation between an event and an argument participating in it. Despite the great progress in event extraction, existing studies still depend on roles pre-defined by domain experts. These studies expose obvious weakness when extending to emerging event types or new domains without available roles. Therefore, more attention and effort needs to be devoted to automatically customizing argument roles. In this paper, we define this essential but under-explored task: open-vocabulary argument role prediction. The goal of this task is to infer a set of argument roles for a given event type. We propose a novel unsupervised framework, RolePred for this task. Specifically, we formulate the role prediction problem as an in-filling task and construct prompts for a pre-trained language model to generate candidate roles. By extracting and analyzing the candidate arguments, the event-specific roles are further merged and selected. To standardize the research of this task, we collect a new human-annotated event extraction dataset including 143 customized argument roles with rich semantics. On this dataset, RolePred outperforms the existing methods by a large margin.
%R 10.18653/v1/2022.findings-emnlp.395
%U https://aclanthology.org/2022.findings-emnlp.395
%U https://doi.org/10.18653/v1/2022.findings-emnlp.395
%P 5404-5418

Markdown (Informal)

[Open-Vocabulary Argument Role Prediction For Event Extraction](https://aclanthology.org/2022.findings-emnlp.395) (Jiao et al., Findings 2022)

• Open-Vocabulary Argument Role Prediction For Event Extraction (Jiao et al., Findings 2022)

ACL

• Yizhu Jiao, Sha Li, Yiqing Xie, Ming Zhong, Heng Ji, and Jiawei Han. 2022. Open-Vocabulary Argument Role Prediction For Event Extraction. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5404–5418, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.