Elizabeth Clark


2021

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Proceedings of the Third Workshop on Narrative Understanding
Nader Akoury | Faeze Brahman | Snigdha Chaturvedi | Elizabeth Clark | Mohit Iyyer | Lara J. Martin
Proceedings of the Third Workshop on Narrative Understanding

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TuringAdvice : A Generative and Dynamic Evaluation of Language UseTuringAdvice: A Generative and Dynamic Evaluation of Language Use
Rowan Zellers | Ari Holtzman | Elizabeth Clark | Lianhui Qin | Ali Farhadi | Yejin Choi
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We propose TuringAdvice, a new challenge task and dataset for language understanding models. Given a written situation that a real person is currently facing, a model must generate helpful advice in natural language. Our evaluation framework tests a fundamental aspect of human language understanding : our ability to use language to resolve open-ended situations by communicating with each other. Empirical results show that today’s models struggle at TuringAdvice, even multibillion parameter models finetuned on 600k in-domain training examples. The best model, T5, writes advice that is at least as helpful as human-written advice in only 14 % of cases ; a much larger non-finetunable GPT3 model does even worse at 4 %. This low performance reveals language understanding errors that are hard to spot outside of a generative setting, showing much room for progress.

2020

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Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events
Claire Bonial | Tommaso Caselli | Snigdha Chaturvedi | Elizabeth Clark | Ruihong Huang | Mohit Iyyer | Alejandro Jaimes | Heng Ji | Lara J. Martin | Ben Miller | Teruko Mitamura | Nanyun Peng | Joel Tetreault
Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events

2019

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Proceedings of the First Workshop on Narrative Understanding
David Bamman | Snigdha Chaturvedi | Elizabeth Clark | Madalina Fiterau | Mohit Iyyer
Proceedings of the First Workshop on Narrative Understanding

2018

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Neural Text Generation in Stories Using Entity Representations as Context
Elizabeth Clark | Yangfeng Ji | Noah A. Smith
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We introduce an approach to neural text generation that explicitly represents entities mentioned in the text. Entity representations are vectors that are updated as the text proceeds ; they are designed specifically for narrative text like fiction or news stories. Our experiments demonstrate that modeling entities offers a benefit in two automatic evaluations : mention generation (in which a model chooses which entity to mention next and which words to use in the mention) and selection between a correct next sentence and a distractor from later in the same story. We also conduct a human evaluation on automatically generated text in story contexts ; this study supports our emphasis on entities and suggests directions for further research.

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Sounding Board : A User-Centric and Content-Driven Social Chatbot
Hao Fang | Hao Cheng | Maarten Sap | Elizabeth Clark | Ari Holtzman | Yejin Choi | Noah A. Smith | Mari Ostendorf
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

We present Sounding Board, a social chatbot that won the 2017 Amazon Alexa Prize. The system architecture consists of several components including spoken language processing, dialogue management, language generation, and content management, with emphasis on user-centric and content-driven design. We also share insights gained from large-scale online logs based on 160,000 conversations with real-world users.