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, M. H. Tessler, and M. Franke (2018). Probabilistic language understanding: An introduction to the Rational Speech Act framework. Retrieved from https://www.problang.org.

Useful resources

Acknowledgments

This webbook grew out of a course taught by the first two 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!