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.

研究探讨了一种新的常识推断任务，通过构建新的众包语料库并使用神经编码器 - 解码器模型，成功地分析先前未见过的事件并推断参与者的意图和反应，揭示了现代电影剧本中普遍存在的隐性性别不平等问题。