Story Cloze Task : UW NLP System

@inproceedings{Schwartz2017StoryCT,
  title={Story Cloze Task : UW NLP System},
  author={Roy Schwartz and Maarten Sap and Ioannis Konstas and Leila Zilles and Yejin Choi and Noah A. Smith},
  year={2017}
}
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). 
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