A recursive dialogue game framework with optimal Policy offering personalized computer-assisted language learning

Abstract

This paper introduces a new recursive dialogue game framework for personalized computer-assisted language learning. A series of sub-dialogue trees are cascaded into a loop as the script for the game. At each dialogue turn there are a number of training sentences to be selected. The dialogue policy is optimized to offer the most appropriate training sentence for an individual learner at each dialogue turn considering the learning status, such that the learner can have the scores for all pronunciation units exceeding a pre-defined threshold in minimum number of turns. The policy is modeled as a Markov Decision Process (MDP) with high dimensional continuous state space. Experiments demonstrate promising results for the approach.