Learning in the rational speech acts model

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When a person says something that has multiple possible interpretations, which interpretation stands out as the most likely intended meaning often depends on context outside the utterance itself: salient objects in the environment, utterances the speaker could have chosen but didn't, common-sense knowledge, etc. Systematically predicting these contextual effects is a major unsolved problem in computational natural language understanding. A recently-developed framework, known in cognitive science as the rational speech acts (RSA) model, proposes that speaker and listener reason probabilistically about each other's goals and private knowledge to produce interpretations that differ from literal meanings. The framework has shown promising experimental results in predicting a wide variety of previously hard-to-model contextual effects. This dissertation describes a variety of methods combining RSA approaches to context modeling with machine learning methods of language understanding and production. Learning meanings of utterances from examples avoids the need to build an impractically large, brittle lexicon, and having models of both speaker and listener also provides a way to reduce the search space by sampling likely subsets of possible utterances and meanings. Using recently-collected corpora of human utterances in simple language games, I show that a combination of RSA and machine learning yields more human-like models of utterances and interpretations than straightforward machine learning classifiers. Furthermore, the RSA insight relating the listener and speaker roles enables the use of a generation model to improve understanding, as well as suggesting a new way to evaluate natural language generation systems in terms of an understanding task.


Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2018; ©2018
Publication date 2018; 2018
Issuance monographic
Language English


Author Monroe, William Charles
Degree supervisor Jurafsky, Dan, 1962-
Degree supervisor Potts, Christopher, 1977-
Thesis advisor Jurafsky, Dan, 1962-
Thesis advisor Potts, Christopher, 1977-
Thesis advisor Goodman, Noah
Degree committee member Goodman, Noah
Associated with Stanford University, Computer Science Department.


Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Will Monroe.
Note Submitted to the Department of Computer Science.
Thesis Thesis Ph.D. Stanford University 2018.
Location electronic resource

Access conditions

© 2018 by William Charles Monroe
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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