Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events
Miguel Ballesteros, Rishita Anubhai, Shuai Wang, Nima Pourdamghani, Yogarshi Vyas, Jie Ma, Parminder Bhatia, Kathleen McKeown, Yaser Al-Onaizan
Abstract
In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations. Our proposed models receive a pair of events within a span of text as input and they identify temporal relations (Before, After, Equal, Vague) between them. Given that a key challenge with this task is the scarcity of annotated data, our models rely on either pretrained representations (i.e. RoBERTa, BERT or ELMo), transfer and multi-task learning (by leveraging complementary datasets), and self-training techniques. Experiments on the MATRES dataset of English documents establish a new state-of-the-art on this task.- Anthology ID:
- 2020.emnlp-main.436
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5412–5417
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.436
- DOI:
- 10.18653/v1/2020.emnlp-main.436
- Cite (ACL):
- Miguel Ballesteros, Rishita Anubhai, Shuai Wang, Nima Pourdamghani, Yogarshi Vyas, Jie Ma, Parminder Bhatia, Kathleen McKeown, and Yaser Al-Onaizan. 2020. Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5412–5417, Online. Association for Computational Linguistics.
- Cite (Informal):
- Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events (Ballesteros et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2020.emnlp-main.436.pdf