Event2Mind: Commonsense Inference on Events, Intents, and Reactions

Hannah Rashkin, Maarten Sap, Emily Allaway, Noah A. Smith, Yejin Choi

We investigate a new commonsense inference task: given an event described in a short free-form text ({``X drinks coffee in the morning''}), a system reasons about the likely intents ({``X wants to stay awake''}) and reactions ({``X feels alert''}) of the event's participants. To support this study, we construct a new crowdsourced corpus of 25,000 event phrases covering a diverse range of everyday events and situations. We report baseline performance on this task, demonstrating that neural encoder-decoder models can successfully compose embedding representations of previously unseen events and reason about the likely intents and reactions of the event participants. In addition, we demonstrate how commonsense inference on people's intents and reactions can help unveil the implicit gender inequality prevalent in modern movie scripts.