ACL 2021short61 citations
TIMERS: Document-level Temporal Relation Extraction
Puneet Mathur, Rajiv Jain, Franck Dernoncourt, Vlad Morariu, Quan Hung Tran, Dinesh Manocha
Abstract
We present TIMERS - a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language. Our proposed method leverages rhetorical discourse features and temporal arguments from semantic role labels, in addition to traditional local syntactic features, trained through a Gated Relational-GCN. Extensive experiments show that the proposed model outperforms previous methods by 5-18% on the TDDiscourse, TimeBank-Dense, and MATRES datasets due to our discourse-level modeling.
BibTeX
@inproceedings{mathur-etal-2021-timers,
title = "{TIMERS}: Document-level Temporal Relation Extraction",
author = "Mathur, Puneet and
Jain, Rajiv and
Dernoncourt, Franck and
Morariu, Vlad and
Tran, Quan Hung and
Manocha, Dinesh",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.67/",
doi = "10.18653/v1/2021.acl-short.67",
pages = "524--533"
}