The present course serves as a practical introduction to the Rational Speech Act modeling framework. Little is presupposed beyond a willingness to explore recent progress in formal, implementable models of language understanding.

Main content

1. Introducing the Rational Speech Act framework
   An introduction to language understanding as Bayesian inference

2. Modeling pragmatic inference
   Enriching the literal interpretations

3. Inferring the Question-Under-Discussion
   Non-literal language

4. Combining RSA and compositional semantics
   Jointly inferring parameters and interpretations

5. Fixing free parameters
   Vagueness

6. Expanding our ontology
   Plural predication

7. Extending our models of predication
   Generic language

8. Modeling semantic inference
   Lexical uncertainty

9. Social reasoning about social reasoning
   Politeness

10. Summary and outlook
    Questions about RSA

Appendix

1. Probabilities & Bayes rule (in WebPPL)
   An quick and gentle introduction to probability and Bayes rule (in WebPPL)

2. More on speaker utility
   Derivation of suprisal-based utilities from KL-divergence

3. Utterance costs and utterance priors
   More on utterance costs and utterance priors

4. Bayesian data analysis
   BDA for the RSA reference game model

5. Quantifier choice & approximate number
   Speaker choice of quantifiers for situations where perception of cardinality is uncertain

Citation

G. Scontras and M. H. Tessler (2017). Probabilistic language understanding: An introduction to the Rational Speech Act framework. Retrieved from https://michael-franke.github.io/probLang/

Useful resources

• Probabilistic Models of Cognition: An introduction to computational cognitive science and the probabilistic programming language WebPPL
• The Design and Implementation of Probabilistic Programming Languages: An introduction to probabilistic programming languages, WebPPL in particular
• Modeling Agents with Probabilistic Programs: An introduction to formal models of rational agents using WebPPL
• Pragmatic language interpretation as probabilistic inference: A recent review of the RSA framework targeted at cognitive scientists
• Pragmatic pragmatics, or why Bayes rule is probably important for pragmatics: A recent review of the RSA framework targeted at linguists
• webppl.org: An online editor for WebPPL
• WebPPL documentation
• WebPPL-viz: A summary of the vizualization options in WebPPL
• Forest: A Repository for probabilistic models
• RWebPPL: If you would rather use WebPPL within R
• WebPPL Tutorials: Basic tutorials for WebPPL

Acknowledgments

This webbook grew out of a course taught by the authors at ESSLLI 2016 in Bolzano, Italy. We owe a special debt of gratitude to our first set of students for their patience, insight, and willingness to serve as test subjects. We are also indebted to the authors of the models included in this text—without their work, there would be nothing to teach!