Fine-Grained Temporal Relation Extraction
Siddharth Vashishtha, Benjamin Van Durme, Aaron Steven White
Abstract
We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales. We use this framework to construct the largest temporal relations dataset to date, covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to train models for jointly predicting fine-grained temporal relations and event durations. We report strong results on our data and show the efficacy of a transfer-learning approach for predicting categorical relations.- Anthology ID:
- P19-1280
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2906–2919
- Language:
- URL:
- https://aclanthology.org/P19-1280
- DOI:
- 10.18653/v1/P19-1280
- Cite (ACL):
- Siddharth Vashishtha, Benjamin Van Durme, and Aaron Steven White. 2019. Fine-Grained Temporal Relation Extraction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2906–2919, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Fine-Grained Temporal Relation Extraction (Vashishtha et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/P19-1280.pdf
- Data
- English Web Treebank