Charles Sutton


2018

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Deep Dungeons and Dragons: Learning Character-Action Interactions from Role-Playing Game Transcripts
Annie Louis | Charles Sutton
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

An essential aspect to understanding narratives is to grasp the interaction between characters in a story and the actions they take. We examine whether computational models can capture this interaction, when both character attributes and actions are expressed as complex natural language descriptions. We propose role-playing games as a testbed for this problem, and introduce a large corpus of game transcripts collected from online discussion forums. Using neural language models which combine character and action descriptions from these stories, we show that we can learn the latent ties. Action sequences are better predicted when the character performing the action is also taken into account, and vice versa for character attributes.

2006

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Proceedings of the Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing
Ryan McDonald | Charles Sutton | Hal Daumé III | Andrew McCallum | Fernando Pereira | Jeff Bilmes
Proceedings of the Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing

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Reducing Weight Undertraining in Structured Discriminative Learning
Charles Sutton | Michael Sindelar | Andrew McCallum
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference

2005

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Composition of Conditional Random Fields for Transfer Learning
Charles Sutton | Andrew McCallum
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

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Joint Parsing and Semantic Role Labeling
Charles Sutton | Andrew McCallum
Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)