Temporal Event Reasoning Using Multi-source Auxiliary Learning Objectives

Xin Dong, Tanay Kumar Saha, Ke Zhang, Joel R. Tetreault, Alejandro Jaimes, Gerard de Melo

Abstract: Temporal event reasoning is vital in modern information-driven applications operating on news articles, social media, financial reports, etc. Recent works train deep neural nets to infer temporal events and relations from text. We improve upon the state-of-the-art by proposing an approach that injects additional temporal knowledge into the pre-trained model from two sources: (i) part-of-speech tagging and (ii) question constraints. Auxiliary learning objectives allow us to incorporate this temporal information into the training process. Our experiments show that these types of multi-source auxiliary learning objectives lead to better temporal reasoning. Our model improves over the state-of-the-art model on the TORQUE question answering benchmark by 1.1% and on the MATRES relation extraction benchmark by 2.8% in F1 score.