  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 event 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 F1 improved by 10% and 6.8% on two benchmark
datasets respectively.
