  We design and build the first neural temporal dependency parser. It utilizes
a neural ranking model with minimal feature engineering, and parses time
expressions and events in a text into a temporal dependency tree structure. We
evaluate our parser on two domains: news reports and narrative stories. In a
parsing-only evaluation setup where gold time expressions and events are
provided, our parser reaches 0.81 and 0.70 f-score on unlabeled and labeled
parsing respectively, a result that is very competitive against alternative
approaches. In an end-to-end evaluation setup where time expressions and events
are automatically recognized, our parser beats two strong baselines on both
data domains. Our experimental results and discussions shed light on the nature
of temporal dependency structures in different domains and provide insights
that we believe will be valuable to future research in this area.
