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.
The recent work of REF introduced a commonsense inference task about events and mental states: given an event described in natural language, the task is to generate the reaction and intent of actors involved in the event.
Event2Mind: Commonsense Inference on Events, Intents, and Reactions
ArXiv
ArXiv
Computer Science