Temporal Reasoning in Natural Language Inference

Abstract

We introduce five new natural language inference (NLI) datasets focused on temporal reasoning. We recast four existing datasets annotated for event duration-how long an event lasts-and event ordering-how events are temporally arranged-into more than one million NLI examples. We use these datasets to investigate how well neural models trained on a popular NLI corpus capture these forms of temporal reasoning.