Story Cloze Task: UW NLP System

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


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).
Anthology ID:
W17-0907
Volume:
Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics
Month:
April
Year:
2017
Address:
Valencia, Spain
Venues:
LSDSem | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
52–55
Language:
URL:
https://aclanthology.org/W17-0907
DOI:
10.18653/v1/W17-0907
Bibkey:
Cite (ACL):
Roy Schwartz, Maarten Sap, Ioannis Konstas, Leila Zilles, Yejin Choi, and Noah A. Smith. 2017. Story Cloze Task: UW NLP System. In Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics, pages 52–55, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Story Cloze Task: UW NLP System (Schwartz et al., 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-0907.pdf