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.
|Task||Dataset||Model||Metric name||Metric value||Global rank||Compare|
|Common Sense Reasoning||Event2Mind||BiRNN 100d||Dev||4.25||# 1|
|Common Sense Reasoning||Event2Mind||BiRNN 100d||Test||4.22||# 1|
|Common Sense Reasoning||Event2Mind||ConvNet||Dev||4.44||# 2|
|Common Sense Reasoning||Event2Mind||ConvNet||Test||4.40||# 2|