Rational Speech Act

Rational Speech Act (RSA) models aim to computationally model human communication by analyzing how speakers choose utterances and listeners interpret them, considering both the literal meaning and the communicative context. Current research focuses on improving RSA's efficiency through techniques like amortized inference and incorporating factors like cultural differences and speaker-listener disparities into model architectures, often leveraging Bayesian approaches and deep learning methods. These advancements enhance the accuracy and applicability of RSA models in various domains, including cross-cultural communication, program synthesis, and referring expression generation, ultimately contributing to a deeper understanding of human pragmatics and informing the development of more natural and effective AI agents.