How can I help you? User instructions in telephone calls

We a small corpus of instructions given in phone calls to customers who need support for programming their universal remote control, to make it suitable for their particular TV set VCR, Audio, etc. Typically, in these calls the operator or 'agent' coaches the client while the client is performing actions with the equipment (turning it on, pressing buttons and codes, directing it towards the TV, etc.). We compared these oral instructions with the concept of a 'streamlined step procedure' (Farkas, 1999) and other principles that are well-known from the literature about written instructions. Our conclusion is that many problems arise because the operator does not provide 'meta-communication' about the goals that have to be achieved, and because the feedback given by the client is neglected or misinterpreted

Individual and Domain Adaptation in Sentence Planning for Dialogue

One of the biggest challenges in the development and deployment of spoken dialogue systems is the design of the spoken language generation module. This challenge arises from the need for the generator to adapt to many features of the dialogue domain, user population, and dialogue context. A promising approach is trainable generation, which uses general-purpose linguistic knowledge that is automatically adapted to the features of interest, such as the application domain, individual user, or user group. In this paper we present and evaluate a trainable sentence planner for providing restaurant information in the MATCH dialogue system. We show that trainable sentence planning can produce complex information presentations whose quality is comparable to the output of a template-based generator tuned to this domain. We also show that our method easily supports adapting the sentence planner to individuals, and that the individualized sentence planners generally perform better than models trained and tested on a population of individuals. Previous work has documented and utilized individual preferences for content selection, but to our knowledge, these results provide the first demonstration of individual preferences for sentence planning operations, affecting the content order, discourse structure and sentence structure of system responses. Finally, we evaluate the contribution of different feature sets, and show that, in our application, n-gram features often do as well as features based on higher-level linguistic representations