  We present a novel semantic framework for modeling temporal relations and
event durations that maps pairs of events to real-valued scales. We use this
framework to construct the largest temporal relations dataset to date, covering
the entirety of the Universal Dependencies English Web Treebank. We use this
dataset to train models for jointly predicting fine-grained temporal relations
and event durations. We report strong results on our data and show the efficacy
of a transfer-learning approach for predicting categorical relations.
