  We propose a novel deep structured learning framework for event temporal
relation extraction. The model consists of 1) a recurrent neural network (RNN)
to learn scoring functions for pair-wise relations, and 2) a structured support
vector machine (SSVM) to make joint predictions. The neural network
automatically learns representations that account for long-term contexts to
provide robust features for the structured model, while the SSVM incorporates
domain knowledge such as transitive closure of temporal relations as
constraints to make better globally consistent decisions. By jointly training
the two components, our model combines the benefits of both data-driven
learning and knowledge exploitation. Experimental results on three high-quality
event temporal relation datasets (TCR, MATRES, and TB-Dense) demonstrate that
incorporated with pre-trained contextualized embeddings, the proposed model
achieves significantly better performances than the state-of-the-art methods on
all three datasets. We also provide thorough ablation studies to investigate
our model.
