Story Cloze Task : UW NLP System

Roy Schwartz, Maarten Sap, Ioannis Konstas, Leila Zilles, Yejin Choi, Noah A. Smith

Research output: Contribution to conferencePaper

Abstract

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).
Original languageEnglish
Pages52-55
Number of pages4
DOIs
Publication statusPublished - Apr 2017
Event2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics 2017 - Valencia, Spain
Duration: 3 Apr 20173 Apr 2017

Workshop

Workshop2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics 2017
Abbreviated titleLSDSEM 2017
CountrySpain
CityValencia
Period3/04/173/04/17

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  • Cite this

    Schwartz, R., Sap, M., Konstas, I., Zilles, L., Choi, Y., & Smith, N. A. (2017). Story Cloze Task : UW NLP System. 52-55. Paper presented at 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics 2017, Valencia, Spain. https://doi.org/10.18653/v1/W17-0907