Do I know what I want to say? Modeling meaning uncertainty in RSA

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, short

Summary

Anzi Wang, Carolyn Jane Anderson, and Grusha Prasad introduce um-RSA, a novel method designed to integrate meaning uncertainty into the Rational Speech Act (RSA) framework. Traditional RSA models typically assume speakers are certain about their communicated meaning, an assumption the authors challenge in specific contexts. Their work, presented in the Proceedings of the Society for Computation in Linguistics 2026, explores two sources of meaning uncertainty: Counting-Uncertainty from numerical cognition and Discounting-Uncertainty from behavioral economics. Through generating predictions and testing them with two human experiments, the researchers demonstrate that um-RSA effectively explains variations in uncertainty expression usage, a capability lacking in the standard RSA framework. This highlights the significant utility of explicitly modeling meaning uncertainty.

Key takeaway

For NLP engineers developing pragmatic language models, understanding speaker uncertainty is crucial. If your current models rely on the Rational Speech Act framework, consider integrating meaning uncertainty using approaches like um-RSA to enhance predictive accuracy for natural language expressions. This can improve model robustness in contexts where speakers are not fully certain, leading to more nuanced and human-like communication simulations.

Key insights

um-RSA models speaker meaning uncertainty, improving Rational Speech Act framework predictions for linguistic expression.

Principles

• Speaker meaning uncertainty impacts communication.
• Standard RSA overlooks crucial contextual nuances.
• Behavioral economics informs linguistic models.

Method

um-RSA incorporates meaning uncertainty into the RSA framework. It generates predictions from specific uncertainty hypotheses (e.g., Counting-Uncertainty, Discounting-Uncertainty) and validates them via human experiments.

In practice

• Integrate uncertainty into pragmatic models.
• Design experiments for linguistic ambiguity.

Topics

• Rational Speech Act
• Meaning Uncertainty
• Computational Pragmatics
• Linguistic Modeling
• Human Experiments
• Numerical Cognition

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.