Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction
Haoyang Wen, Heng Ji · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
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 a Stack-Propagation framework to incorporate predicted relative event time for temporal relation classification and keep the differentiability.Our experiments on MA-TRES dataset show that our model can significantly improve the RoBERTa-based baseline and achieve state-of-the-art performance.1