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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.73.pdf