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.- Anthology ID:
- 2024.findings-eacl.126
- Volume:
- Findings of the Association for Computational Linguistics: EACL 2024
- Month:
- March
- Year:
- 2024
- Address:
- St. Julian’s, Malta
- Editors:
- Yvette Graham, Matthew Purver
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1860–1873
- Language:
- URL:
- https://aclanthology.org/2024.findings-eacl.126
- DOI:
- Cite (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.
- Cite (Informal):
- QAEVENT: Event Extraction as Question-Answer Pairs Generation (Choudhary & Du, Findings 2024)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2024.findings-eacl.126.pdf