Weakly and Strongly Constrained Dialogues for Language Learning

We present two dialogue systems for language learning which both restrict the dialog to a specific domain thereby promoting robustness and the learning of a given vocabulary. The systems vary in how much they constrain the learner’s answer : one system places no other constrain on the learner than that provided by the restricted domain and the dialog context ; the other provides the learner with an exercise whose solution is the expected answer. The first system uses supervised learning for simulating a human tutor whilst the second one uses natural language generation techniques to produce grammar exercises which guide the learner toward the expected answer.