Ordinal regression for interaction quality prediction

The automatic prediction of the quality of a dialogue is useful to keep track of a spoken dialogue system’s performance and, if necessary, adapt its behavior. Classifiers and regression models have been suggested to make this prediction. The parameters of these models are learnt from a corpus of dialogues evaluated by users or experts. In this paper, we propose to model this task as an ordinal regression problem. We apply support vector machines for ordinal regression on a corpus of dialogues where each system-user exchange was given a rate on a scale of 1 to 5 by experts. Compared to previous models proposed in the literature, the ordinal regression predictor has significantly better results according to the following evaluation metrics: Cohen’s agreement rate with experts ratings, Spearman’s rank correlation coefficient, and Euclidean and Manhattan errors.