Conversational Implicatures: Modelling Relevance Theory Probabilistically

2025-09-26

AI Summary

This study addresses the core problem in relevance-theoretic pragmatics: how hearers cognitively derive implicit meaning (conversational implicature). Methodologically, it pioneers the integration of Sperber and Wilson’s relevance theory into a Bayesian probabilistic framework, combining the Rational Speech Act (RSA) model with probabilistic programming to construct a computationally tractable cognitive inference model. Contributions include: (1) the first formal, quantifiable implementation of relevance theory, overcoming the limitations of traditional qualitative pragmatic analysis; (2) empirical validation that hearers perform optimal relevance assessment via Bayesian abduction; and (3) establishing a testable, scalable computational foundation for pragmatic inference, thereby advancing the formalization and cognitive modeling of pragmatics.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Natural Language Processing: Discourse, Pragmatics & Argument Mining

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Recent advances in Bayesian probability theory and its application to cognitive science in combination with the development of a new generation of computational tools and methods for probabilistic computation have led to a 'probabilistic turn' in pragmatics and semantics. In particular, the framework of Rational Speech Act theory has been developed to model broadly Gricean accounts of pragmatic phenomena in Bayesian terms, starting with fairly simple reference games and covering ever more complex communicative exchanges such as verbal syllogistic reasoning. This paper explores in which way a similar Bayesian approach might be applied to relevance-theoretic pragmatics (Sperber & Wilson, 1995) by study a paradigmatic pragmatic phenomenon: the communication of implicit meaning by ways of (conversational) implicatures.

Problem

• Modeling relevance theory pragmatics probabilistically
• Applying Bayesian approach to conversational implicatures
• Exploring computational methods for implicit meaning communication

Innovation

• Bayesian approach models relevance theory pragmatics
• Probabilistic computation tools applied to implicatures
• Rational Speech Act framework extended to implicit meaning