A Model of Conversational Scalar Implicature in Computational Pragmatics

A computational pragmatic model of conversational scalar implicature is established by using Bayes' theorem and its statistical methods, and a small man-machine dialogue program is implemented based on it. Firstly, four ideas that Bayesian methods can be used in computational pragmatics are extracted, and their fit with scalar implicature is demonstrated. Then, by determining state set, prior and conditional probability, the corresponding conversational implicature is obtained after calculating the posterior probability. Finally, based on this process, a man-machine dialogue program for the probability analysis of implicature is programmed. This research shows that the conversational implicature module in natural language processing can be implemented by Bayesian-based causal inference.