DocTime: A Document-level Temporal Dependency Graph Parser
Puneet Mathur, Vlad Morariu, Verena Kaynig-Fittkau, Jiuxiang Gu, Franck Dernoncourt, Quan Tran, Ani Nenkova, Dinesh Manocha, Rajiv Jain
Abstract
We introduce DocTime - a novel temporal dependency graph (TDG) parser that takes as input a text document and produces a temporal dependency graph. It outperforms previous BERT-based solutions by a relative 4-8% on three datasets from modeling the problem as a graph network with path-prediction loss to incorporate longer range dependencies. This work also demonstrates how the TDG graph can be used to improve the downstream tasks of temporal questions answering and NLI by a relative 4-10% with a new framework that incorporates the temporal dependency graph into the self-attention layer of Transformer models (Time-transformer). Finally, we develop and evaluate on a new temporal dependency graph dataset for the domain of contractual documents, which has not been previously explored in this setting.- Anthology ID:
- 2022.naacl-main.73
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 993–1009
- Language:
- URL:
- https://aclanthology.org/2022.naacl-main.73
- DOI:
- 10.18653/v1/2022.naacl-main.73
- Cite (ACL):
- Puneet Mathur, Vlad Morariu, Verena Kaynig-Fittkau, Jiuxiang Gu, Franck Dernoncourt, Quan Tran, Ani Nenkova, Dinesh Manocha, and Rajiv Jain. 2022. DocTime: A Document-level Temporal Dependency Graph Parser. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 993–1009, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- DocTime: A Document-level Temporal Dependency Graph Parser (Mathur et al., NAACL 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.naacl-main.73.pdf
- Data
- TimeQA