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
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2906–2919
URL:
https://www.aclweb.org/anthology/P19-1280
DOI:
10.18653/v1/P19-1280
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PDF:
https://www.aclweb.org/anthology/P19-1280.pdf