This paper describes University of Washington NLP's submission for the Linking Models of Lexical, Sentential and Discourse-level Semantics (LSDSem 2017) shared task-the Story Cloze Task. Our system is a linear classifier with a variety of features, including both the scores of a neural language model and style features. We report 75.2% accuracy on the task. A further discussion of our results can be found in Schwartz et al. (2017) .
The highest performing model with 75% accuracy used a linear regression classifier with several features such as neural language models and stylistic features to model the story coherence REF .
Story Cloze Task: UW NLP System
LSDSem@EACL
Psychology Computer Science