Papers › Event2Mind: Commonsense Inference on Events, Intents, and Reactions
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Common Sense Reasoning | Event2Mind dev | ConvNet | Average Cross-Ent | 4.44 | #1 of 2 | Archive leaderboard | report |
| Common Sense Reasoning | Event2Mind dev | BiRNN 100d | Average Cross-Ent | 4.25 | #2 of 2 | Archive leaderboard | report |
| Common Sense Reasoning | Event2Mind test | ConvNet | Average Cross-Ent | 4.4 | #1 of 7 | Archive leaderboard | report |
| Common Sense Reasoning | Event2Mind test | BiRNN 100d | Average Cross-Ent | 4.22 | #2 of 7 | Archive leaderboard | report |
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