  Extracting temporal relations (e.g., before, after, concurrent) among events
is crucial to natural language understanding. Previous studies mainly rely on
neural networks to learn effective features or manual-crafted linguistic
features for temporal relation extraction, which usually fail when the context
between two events is complex or wide. Inspired by the examination of available
temporal relation annotations and human-like cognitive procedures, we propose a
new Temporal Graph Transformer network to (1) explicitly find the connection
between two events from a syntactic graph constructed from one or two
continuous sentences, and (2) automatically locate the most indicative temporal
cues from the path of the two event mentions as well as their surrounding
concepts in the syntactic graph with a new temporal-oriented attention
mechanism. Experiments on MATRES and TB-Dense datasets show that our approach
significantly outperforms previous state-of-the-art methods on both end-to-end
temporal relation extraction and temporal relation classification.
