Open AccessProceedings Article
Learning Latent Personas of Film Characters
David Bamman,Brendan O'Connor,Noah A. Smith +2 more
- pp 352-361
TLDR
Two latent variable models for learning character types, or personas, in film, are presented, in which a persona is defined as a set of mixtures over latent lexical classes.Abstract:
We present two latent variable models for learning character types, or personas, in film, in which a persona is defined as a set of mixtures over latent lexical classes. These lexical classes capture the stereotypical actions of which a character is the agent and patient, as well as attributes by which they are described. As the first attempt to solve this problem explicitly, we also present a new dataset for the text-driven analysis of film, along with a benchmark testbed to help drive future work in this area.read more
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The NarrativeQA Reading Comprehension Challenge
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TL;DR: A new dataset and set of tasks in which the reader must answer questions about stories by reading entire books or movie scripts are presented, designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience.
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Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences
TL;DR: The dataset is the first to study multi-sentence inference at scale, with an open-ended set of question types that requires reasoning skills, and finds human solvers to achieve an F1-score of 88.1%.
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A Bayesian Mixed Effects Model of Literary Character
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Event Representations for Automated Story Generation with Deep Neural Nets
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Proceedings Article
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