One of the most robust findings of studies of human-human dialogue is that people adapt their utterances to their conversational partners. However, spoken language generators are limited in their ability to adapt to individual users. While statistical models of language generation have the potential for individual adaptation, we know of no experiments showing this. In this paper, we utilize one statistical method, boosting, to train a spoken language generator for individual users. We show that individualized models perform better than models based on sets of users, and describe differences in the learned individual models arising from the linguistic preferences of users.
['François Mairesse', 'Marilyn A. Walker']
Learning to Personalize Spoken Generation for Dialogue Systems