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
- Editors:
- Michael Roth, Nasrin Mostafazadeh, Nathanael Chambers, Annie Louis
- Venue:
- LSDSem
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 52–55
- Language:
- URL:
- https://aclanthology.org/W17-0907
- DOI:
- 10.18653/v1/W17-0907
- 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., LSDSem 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/W17-0907.pdf