Abstract
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.