Fine-Grained Temporal Relation Extraction

ACL 2019

Fine-Grained Temporal Relation Extraction

Jan 31, 2021
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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. Authors: Siddharth Vashishtha, Benjamin Van Durme, Aaron Steven White (University of Rochester)

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