Event2Mind: Commonsense Inference on Events, Intents, and Reactions

ACL 2018 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.

Full paper

Evaluation


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