  Natural language understanding is a challenging problem that covers a wide
range of tasks. While previous methods generally train each task separately, we
consider combining the cross-task features to enhance the task performance. In
this paper, we incorporate the logic information with the help of the Natural
Language Inference (NLI) task to the Story Cloze Test (SCT). Previous work on
SCT considered various semantic information, such as sentiment and topic, but
lack the logic information between sentences which is an essential element of
stories. Thus we propose to extract the logic information during the course of
the story to improve the understanding of the whole story. The logic
information is modeled with the help of the NLI task. Experimental results
prove the strength of the logic information.
