Leila Zilles


2017

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Story Cloze Task: UW NLP System
Roy Schwartz | Maarten Sap | Ioannis Konstas | Leila Zilles | Yejin Choi | Noah A. Smith
Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics

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).

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The Effect of Different Writing Tasks on Linguistic Style: A Case Study of the ROC Story Cloze Task
Roy Schwartz | Maarten Sap | Ioannis Konstas | Leila Zilles | Yejin Choi | Noah A. Smith
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

A writer’s style depends not just on personal traits but also on her intent and mental state. In this paper, we show how variants of the same writing task can lead to measurable differences in writing style. We present a case study based on the story cloze task (Mostafazadeh et al., 2016a), where annotators were assigned similar writing tasks with different constraints: (1) writing an entire story, (2) adding a story ending for a given story context, and (3) adding an incoherent ending to a story. We show that a simple linear classifier informed by stylistic features is able to successfully distinguish among the three cases, without even looking at the story context. In addition, combining our stylistic features with language model predictions reaches state of the art performance on the story cloze challenge. Our results demonstrate that different task framings can dramatically affect the way people write.

2013

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Joint Coreference Resolution and Named-Entity Linking with Multi-Pass Sieves
Hannaneh Hajishirzi | Leila Zilles | Daniel S. Weld | Luke Zettlemoyer
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing