Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction

Rujun Han, Qiang Ning, Nanyun Peng
2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)  
We propose a joint event and temporal relation extraction model with shared representation learning and structured prediction. The proposed method has two advantages over existing work. First, it improves event representation by allowing the event and relation modules to share the same contextualized embeddings and neural representation learner. Second, it avoids error propagation in the conventional pipeline systems by leveraging structured inference and learning methods to assign both the
more » ... t labels and the temporal relation labels jointly. Experiments show that the proposed method can improve both event extraction and temporal relation extraction over state-of-the-art systems, with the end-to-end F 1 improved by 10% and 6.8% on two benchmark datasets respectively.
doi:10.18653/v1/d19-1041 dblp:conf/emnlp/HanNP19 fatcat:jee36ibgzjh3tno65p66laxuzq