Fine-Grained Temporal Relation Extraction.

We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales for the purpose of constructing document-level event timelines. 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 standard, categorical TimeML relations.