Temporal Reasoning in Natural Language Inference
Siddharth Vashishtha, Adam Poliak, Yash Kumar Lal, Benjamin Van Durme, Aaron Steven White
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.
Topics & Concepts
Computer scienceInferenceArtificial intelligenceEvent (particle physics)Natural language processingNatural languageDuration (music)Natural (archaeology)HistoryPhysicsArchaeologyQuantum mechanicsArtLiteratureTopic ModelingNatural Language Processing TechniquesSpeech and dialogue systems