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One of the biggest challenges in the development and deployment of spokendialogue systems is the design of the spoken language generation module. Thischallenge arises from the need for the generator to adapt to many features ofthe dialogue domain, user population, and dialogue context. A promisingapproach is trainable generation, which uses general-purpose linguisticknowledge that is automatically adapted to the features of interest, such asthe application domain, individual user, or user group. In this paper wepresent and evaluate a trainable sentence planner for providing restaurantinformation in the MATCH dialogue system. We show that trainable sentenceplanning can produce complex information presentations whose quality iscomparable to the output of a template-based generator tuned to this domain. Wealso show that our method easily supports adapting the sentence planner toindividuals, and that the individualized sentence planners generally performbetter than models trained and tested on a population of individuals. Previouswork has documented and utilized individual preferences for content selection,but to our knowledge, these results provide the first demonstration ofindividual preferences for sentence planning operations, affecting the contentorder, discourse structure and sentence structure of system responses. Finally,we evaluate the contribution of different feature sets, and show that, in ourapplication, n-gram features often do as well as features based on higher-levellinguistic representations.

Individual and Domain Adaptation in Sentence Planning for Dialogue