Automatic Learning of Sentence Dependencies in Spoken Dialogues

This paper describes a strategy followed by the dialogue manager of a database inquiry system to assess the reliability of input utterances. For this purpose, binary classification trees are used, in such a way that the assessment capability can be automatically learnt from samples. As a starting point, training of such trees for classification of ATIS-3 sentences in terms of A, D and X classes was done; experimental results show that this assessment technique can be effectively exploited and integrated into the dialogue manager under development