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 language | English |
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Title of host publication | LSDSem 2017 - 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-Level Semantics, Proceedings of the Workshop |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 52-55 |
Number of pages | 4 |
ISBN (Electronic) | 9781945626401 |
State | Published - 2017 |
Externally published | Yes |
Event | 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-Level Semantics, LSDSem 2017 - Valencia, Spain Duration: 3 Apr 2017 → … |
Publication series
Name | LSDSem 2017 - 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-Level Semantics, Proceedings of the Workshop |
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Conference
Conference | 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-Level Semantics, LSDSem 2017 |
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Country/Territory | Spain |
City | Valencia |
Period | 3/04/17 → … |
Bibliographical note
Publisher Copyright:© 2017 Association for Computational Linguistics