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

Fingerprint Dive into the research topics of 'Story Cloze Task : UW NLP System'. Together they form a unique fingerprint.

Cite this