Abstract
Event time is one of the most important features for event-event temporal relation extraction. However, explicit event time information in text is sparse. For example, only about 20% of event mentions in TimeBank-Dense have event-time links. In this paper, we propose a joint model for event-event temporal relation classification and an auxiliary task, relative event time prediction, which predicts the event time as real numbers. We adopt the Stack-Propagation framework to incorporate predicted relative event time for temporal relation classification and keep the differentiability. Our experiments on MATRES dataset show that our model can significantly improve the RoBERTa-based baseline and achieve state-of-the-art performance.- Anthology ID:
- 2021.emnlp-main.815
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10431–10437
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.815
- DOI:
- 10.18653/v1/2021.emnlp-main.815
- Cite (ACL):
- Haoyang Wen and Heng Ji. 2021. Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 10431–10437, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction (Wen & Ji, EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.emnlp-main.815.pdf
- Code
- wenhycs/emnlp2021-utilizing-relative-event-time-to-enhance-event-event-temporal-relation-extraction