Exécution de notre outil de synthèse sur un nouvel article
Vérifiez l’article ci-dessous, puis lancez sa synthèse.
Temporal Reasoning in Natural Language Inference
Siddharth Vashishtha (University of Rochester), Adam Poliak (Barnard College), Yash Kumar Lal (Stony Brook University), Benjamin Van Durme (Johns Hopkins University), Aaron Steven White (University of Rochester)
Résumé : 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.