A practical introduction to the Rational Speech Act modeling framework

What it is about

A tutorial and critical survey of Rational Speech Act (RSA) models. It derives the vanilla model (literal listener L0, speaker S1 with rationality α and utterance cost, pragmatic listener L1) and shows that S1's choice factorises into truth, informativity and cost, read as Gricean Quality, Quantity and Manner. It then lists the extensions as answers to kinds of uncertainty: lexical uncertainty (a threshold for vague adjectives such as heavy, inferred jointly with the state; distributive/collective readings), uncertainty about the speaker's goal as a QUD that partitions the states (hyperbole, irony via an affect dimension), uncertainty about the context (comparison classes for tall; «wonky worlds» when the utterance is unexpected), the speaker's partial knowledge (fewer «not all» implicatures when the speaker has not seen everything), and social utility mixed with informativity (politeness, white lies). The last part covers practice: choosing states and alternatives, estimating priors, fitting α, linking model output to data, Bayesian model comparison, and the open problem of compositionality.

Ideas

• I1. Extend prag/src/rsa.rs (exact fractions, given order) with the knowledge-state speaker: the speaker's observation is a set of possible worlds, and S1 is true-and-informative over all of them. Then «some» from a speaker who only looked at part of the boxes yields a weaker «not all» inference. Wire it into prag/src/infer.rs as a cancellation cue: a perception or access frame that is partial («I looked into two of the jars») blocks the scalar implicature, and the explanation says why. Test with a minimal pair (full vs partial access) in rsa::tests.
• I2. Add lexical uncertainty for gradable adjectives as a small exact game: states = heights or sizes on a few bins, a comparison-class prior from the noun (a big mouse vs a big elephant), thresholds as a latent variable. This answers school questions of the kind «Is a big mouse bigger than a small dog?» with a stated reason. Priors as small integer tables per noun class, written by hand or by the teacher once.
• I3. Use the QUD-partition speaker for irony and hyperbole: states carry an affect bit, and a speaker answering the «affect?» QUD can say something literally false. In prag/src/irony.rs the existing incongruity candidates could be checked against this: an evaluation that is false of the narrated state but matches the speaker's negative affect is ironic. Keep it a small game with printed L0/S1/L1 rows, as rsa explain already does.
• I4. Record the paper's caution that α and recursion depth trade off and cannot both be fitted; fix depth at one (L1) in Mova and treat α only as a documented constant, so the RSA tables stay comparable across releases.

For Mova

33. Implicature and presupposition §9 (RSA; Mova's exact RSA) and §4 (scalar implicatures), 34-irony-and-figurative-language.md §3, §5 (irony, hyperbole), 15-adjectives-adverbs.md for gradable adjectives. Code: prag/src/rsa.rs, prag/src/infer.rs, prag/src/irony.rs. Measures: hand-computed tests with negative controls (uninformative utterances return the prior); count of scalar inferences cancelled by partial-access frames on tales.

Topics: Speech act theory · Implicature

Cite

The paper itself, not this card. BibTeX from the library's record of the work. arXiv 2105.09867

@misc{scontras2021practical,
  title = {{A practical introduction to the Rational Speech Act modeling framework}},
  author = {Scontras, Gregory and Tessler, Michael Henry and Franke, Michael},
  year = {2021},
  eprint = {2105.09867},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2105.09867}
}

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