User-tailored generation for spoken dialogue: an experiment

Recent work on evaluation of spoken dialogue systems suggests that the information presentation phase of complex dialogues is often the primary contributor to dialogue duration. Therefore, better algorithms are needed for the presentation of complex information in speech. This paper evaluates the effect of a user model on generation for three dialogue strategies: SUMMARY, COMPARE and RECOMMEND. We present results showing that (a) both COMPARE and RECOMMEND strategies are effective; and (b) the user model is useful.