  Determining temporal relations (e.g., before or after) between events has
been a challenging natural language understanding task, partly due to the
difficulty to generate large amounts of high-quality training data.
Consequently, neural approaches have not been widely used on it, or showed only
moderate improvements. This paper proposes a new neural system that achieves
about 10% absolute improvement in accuracy over the previous best system (25%
error reduction) on two benchmark datasets. The proposed system is trained on
the state-of-the-art MATRES dataset and applies contextualized word embeddings,
a Siamese encoder of a temporal common sense knowledge base, and global
inference via integer linear programming (ILP). We suggest that the new
approach could serve as a strong baseline for future research in this area.
