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    <abstract>We propose a novel representation of document-level events as question and answer pairs (QAEVENT). Under this paradigm: (1) questions themselves can define argument roles without the need for predefined schemas, which will cover a comprehensive list of event arguments from the document; (2) it allows for more scalable and faster annotations from crowdworkers without linguistic expertise. Based on our new paradigm, we collect a novel and wide-coverage dataset. Our examinations show that annotations with the QA representations produce high-quality data for document-level event extraction, both in terms of human agreement level and high coverage of roles comparing to the pre-defined schema. We present and compare representative approaches for generating event question answer pairs on our benchmark.</abstract>
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        <url>https://aclanthology.org/2024.findings-eacl.126</url>
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%0 Conference Proceedings
%T QAEVENT: Event Extraction as Question-Answer Pairs Generation
%A Choudhary, Milind
%A Du, Xinya
%Y Graham, Yvette
%Y Purver, Matthew
%S Findings of the Association for Computational Linguistics: EACL 2024
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F choudhary-du-2024-qaevent
%X We propose a novel representation of document-level events as question and answer pairs (QAEVENT). Under this paradigm: (1) questions themselves can define argument roles without the need for predefined schemas, which will cover a comprehensive list of event arguments from the document; (2) it allows for more scalable and faster annotations from crowdworkers without linguistic expertise. Based on our new paradigm, we collect a novel and wide-coverage dataset. Our examinations show that annotations with the QA representations produce high-quality data for document-level event extraction, both in terms of human agreement level and high coverage of roles comparing to the pre-defined schema. We present and compare representative approaches for generating event question answer pairs on our benchmark.
%U https://aclanthology.org/2024.findings-eacl.126
%P 1860-1873

Markdown (Informal)

[QAEVENT: Event Extraction as Question-Answer Pairs Generation](https://aclanthology.org/2024.findings-eacl.126) (Choudhary & Du, Findings 2024)

• QAEVENT: Event Extraction as Question-Answer Pairs Generation (Choudhary & Du, Findings 2024)

ACL

• Milind Choudhary and Xinya Du. 2024. QAEVENT: Event Extraction as Question-Answer Pairs Generation. In Findings of the Association for Computational Linguistics: EACL 2024, pages 1860–1873, St. Julian’s, Malta. Association for Computational Linguistics.