  Representation and learning of commonsense knowledge is one of the
foundational problems in the quest to enable deep language understanding. This
issue is particularly challenging for understanding casual and correlational
relationships between events. While this topic has received a lot of interest
in the NLP community, research has been hindered by the lack of a proper
evaluation framework. This paper attempts to address this problem with a new
framework for evaluating story understanding and script learning: the 'Story
Cloze Test'. This test requires a system to choose the correct ending to a
four-sentence story. We created a new corpus of ~50k five-sentence commonsense
stories, ROCStories, to enable this evaluation. This corpus is unique in two
ways: (1) it captures a rich set of causal and temporal commonsense relations
between daily events, and (2) it is a high quality collection of everyday life
stories that can also be used for story generation. Experimental evaluation
shows that a host of baselines and state-of-the-art models based on shallow
language understanding struggle to achieve a high score on the Story Cloze
Test. We discuss these implications for script and story learning, and offer
suggestions for deeper language understanding.
