Abstract
Classifying temporal relations between a pair of events is crucial to natural language understanding and a well-known natural language processing task. Given a document and two event mentions, the task is aimed at finding which one started first. We propose an efficient approach for temporal relation classification (TRC) using a boolean question answering (QA) model which we fine-tune on questions that we carefully design based on the TRC annotation guidelines, thereby mimicking the way human annotators approach the task. Our new QA-based TRC model outperforms previous state-of-the-art results by 2.4%.- Anthology ID:
- 2023.findings-acl.116
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1843–1852
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.116
- DOI:
- 10.18653/v1/2023.findings-acl.116
- Cite (ACL):
- Omer Cohen and Kfir Bar. 2023. Temporal Relation Classification using Boolean Question Answering. In Findings of the Association for Computational Linguistics: ACL 2023, pages 1843–1852, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Temporal Relation Classification using Boolean Question Answering (Cohen & Bar, Findings 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.findings-acl.116.pdf