Probabilistic pragmatics

Lecturer: Judith Degen, Stanford University
Room: 1.401
Pragmatics was once thought of as the wastebasket of linguistics: as the caricature went, phenomena that were too complex to handle in the semantics were pushed to the mushy pragmatics, where they were dispatched with hand-wavy just-so stories. Recent developments in cognitive science have led pragmatics to a new period of maturation, facilitated by two important factors: a) the novel application of mathematical modeling techniques, and b) access to rich experimental data. Advances in probabilistic and game-theoretic models that treat pragmatic inference as a problem of social reasoning under uncertainty have yielded testable quantitative predictions about the outcome of many different kinds of pragmatic inference. The phenomena that these types of models have been successfully applied to include scalar implicature, ad hoc Quantity implicatures, M-implicatures, gradable adjectives, and hyperbole, among many others (for a review, see Goodman & Frank, 2016).
The course will introduce students to models of pragmatics that employ probabilistic inference to explain both utterance interpretation and production choices for a variety of phenomena. The basics of fitting experimental data to probabilistic cognitive models will be explained on the basis of case studies of increasing complexity from the recent literature. Students will learn to modify and build their own computational models within the Rational Speech Act framework using the probabilistic programming language WebPPL.

Mon Aug 5th

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    Pragmatics and basics of probability theory

Tue Aug 6th

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    Modeling reference: introduction to the Rational Speech Act model

Wed Aug 7th

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    Modeling scalar implicature

Thu Aug 8th

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    Joint inference: modeling interpretation of gradable adjectives

Fri Aug 9th

Mon Aug 12th

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    Joint inference: modeling QUD effects (scalar implicature, hyperbole)

Tue Aug 13th

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    Overinformative referring expressions

Wed Aug 14th

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    Bayesian Data Analysis: Fitting models to data

Thu Aug 15th

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    Quo vadis, probabilistic pragmatics?

Fri Aug 16th