A recursive dialogue game framework with optimal Policy offering personalized computer-assisted language learning
Pei-Hao Su, Yow-Bang Wang, Tsung-Hsien Wen, Tien-han Yu, Lin-shan Lee · 2013
This paper introduces a new recursive dialogue game frame-work 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 train-ing sentences to be selected. The dialogue policy is optimized to offer the most appropriate training sentence for an individ-ual 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. Experi-ments demonstrate promising results for the approach.