# (RSA)2: A Rhetorical-Strategy-Aware Rational Speech Act Framework for Figurative Language Understanding

arXiv: https://arxiv.org/abs/2506.09301 · HTML: https://mlsanity.com/paper/2506.09301/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [A fine-grained comparison of pragmatic language understanding in humans and language models](https://mlsanity.com/paper/2212.06801/) (2022)
- [Reasoning About Pragmatics with Neural Listeners and Speakers](https://mlsanity.com/paper/1604.00562/) (2016)
- [Testing the Ability of Language Models to Interpret Figurative Language](https://mlsanity.com/paper/2204.12632/) (2022)
- [PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities](https://mlsanity.com/paper/2401.07078/) (2024)
- [Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding](https://mlsanity.com/paper/1703.10186/) (2017)
- [Unified Pragmatic Models for Generating and Following Instructions](https://mlsanity.com/paper/1711.04987/) (2017)
- [Pragmatically Informative Text Generation](https://mlsanity.com/paper/1904.01301/) (2019)
- [Lost in Machine Translation: A Method to Reduce Meaning Loss](https://mlsanity.com/paper/1902.09514/) (2019)
- [Non-literal Understanding of Number Words by Language Models](https://mlsanity.com/paper/2502.06204/) (2025)
- [Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE](https://mlsanity.com/paper/2210.16407/) (2022)
- [Towards a Fully Interpretable and More Scalable RSA Model for Metaphor Understanding](https://mlsanity.com/paper/2404.02983/) (2024)

## Abstract (arXiv)

Figurative language (e.g., irony, hyperbole, understatement) is ubiquitous in human communication, resulting in utterances where the literal and the intended meanings do not match. The Rational Speech Act (RSA) framework, which explicitly models speaker intentions, is the most widespread theory of probabilistic pragmatics, but existing implementations are either unable to account for figurative expressions or require modeling the implicit motivations for using figurative language (e.g., to express joy or annoyance) in a setting-specific way. In this paper, we introduce the Rhetorical-Strategy-Aware RSA (RSA)2 framework which models figurative language use by considering a speaker's employed rhetorical strategy. We show that (RSA)2 enables human-compatible interpretations of non-literal utterances without modeling a speaker's motivations for being non-literal. Combined with LLMs, it achieves state-of-the-art performance on the ironic split of PragMega+, a new irony interpretation dataset introduced in this study.