EMNLP 2021main46 citations
Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction
Abstract
Event time is one of the most important features for event-event temporal relation extraction. However, explicit event time information in text is sparse. For example, only about 20% of event mentions in TimeBank-Dense have event-time links. In this paper, we propose a joint model for event-event temporal relation classification and an auxiliary task, relative event time prediction, which predicts the event time as real numbers. We adopt the Stack-Propagation framework to incorporate predicted relative event time for temporal relation classification and keep the differentiability. Our experiments on MATRES dataset show that our model can significantly improve the RoBERTa-based baseline and achieve state-of-the-art performance.
BibTeX
@inproceedings{wen-ji-2021-utilizing,
title = "Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction",
author = "Wen, Haoyang and
Ji, Heng",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.815/",
doi = "10.18653/v1/2021.emnlp-main.815",
pages = "10431--10437"
}