# Learning in the Rational Speech Acts Model

arXiv: https://arxiv.org/abs/1510.06807 · HTML: https://mlsanity.com/paper/1510.06807/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2015.

## Built on by (most cited first, 16 of 16)

- [Language Model Behavior: A Comprehensive Survey](https://mlsanity.com/paper/2303.11504/) (2023)
- [COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements](https://mlsanity.com/paper/2306.01985/) (2023)
- [Few-shot Language Coordination by Modeling Theory of Mind](https://mlsanity.com/paper/2107.05697/) (2021)
- [On the Critical Role of Conventions in Adaptive Human-AI Collaboration](https://mlsanity.com/paper/2104.02871/) (2021)
- [Interactive Learning from Activity Description](https://mlsanity.com/paper/2102.07024/) (2021)
- [Learning to Mediate Disparities Towards Pragmatic Communication](https://mlsanity.com/paper/2203.13685/) (2022)
- [Emergence of hierarchical reference systems in multi-agent communication](https://mlsanity.com/paper/2203.13176/) (2022)
- [A practical introduction to the Rational Speech Act modeling framework](https://mlsanity.com/paper/2105.09867/) (2021)
- [Generating Pragmatic Examples to Train Neural Program Synthesizers](https://mlsanity.com/paper/2311.05740/) (2023)
- [Amortizing Pragmatic Program Synthesis with Rankings](https://mlsanity.com/paper/2407.02499/) (2024)
- [Cognitive Modeling with Scaffolded LLMs: A Case Study of Referential Expression Generation](https://mlsanity.com/paper/2407.03805/) (2024)
- [Communicating with Speakers and Listeners of Different Pragmatic Levels](https://mlsanity.com/paper/2410.05851/) (2024)
- [Referring Expressions with Rational Speech Act Framework: A Probabilistic Approach](https://mlsanity.com/paper/2205.07795/) (2022)
- [Amortizing Pragmatic Program Synthesis with Rankings](https://mlsanity.com/paper/2309.03225/) (2023)
- [S-MARC: Causal Streaming Reasoning for Full-Duplex Conversational Behavior Modeling](https://mlsanity.com/paper/2602.11065/) (2026)
- [When Contextual Inference Fails: Cancelability in Interactive Instruction Following](https://mlsanity.com/paper/2603.19997/) (2026)

## Abstract (arXiv)

The Rational Speech Acts (RSA) model treats language use as a recursive process in which probabilistic speaker and listener agents reason about each other's intentions to enrich the literal semantics of their language along broadly Gricean lines. RSA has been shown to capture many kinds of conversational implicature, but it has been criticized as an unrealistic model of speakers, and it has so far required the manual specification of a semantic lexicon, preventing its use in natural language processing applications that learn lexical knowledge from data. We address these concerns by showing how to define and optimize a trained statistical classifier that uses the intermediate agents of RSA as hidden layers of representation forming a non-linear activation function. This treatment opens up new application domains and new possibilities for learning effectively from data. We validate the model on a referential expression generation task, showing that the best performance is achieved by incorporating features approximating well-established insights about natural language generation into RSA.