A dialogue game framework with personalized training using reinforcement learning for computer-assisted language learning

Pei-hao Su, Yow-Bang Wang, Tien-han Yu, Lin-Shan Lee

Abstract: We propose a framework for computer-assisted language learning as a pedagogical dialogue game. The goal is to offer personalized learning sentences on-line for each individual learner considering the learner's learning status, in order to strike a balance between more practice on poorly-pronounced units and complete practice on the whole set of pronunciation units. This objective is achieved using a Markov decision process (MDP) trained with reinforcement learning using simulated learners generated from real learner data. Preliminary experimental results on a subset of the example dialogue script show the effectiveness of the framework.