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We are developing a corpus-based approach for the prediction of help-desk responses from features in customers' emails, where responses are represented at two levels of granularity: document and sentence. We present an automatic evaluation of the responses generated by our system, as well as a manual one involving human judges. The automatic evaluation involves textual comparisons between generated responses and responses composed by the help-desk operators. The results show that both levels of granularity produce good responses, addressing inquiries of different kinds. The human-based evaluation measures response in- formativeness, and confirms our conclusion that both levels of granularity produce useful responses.

We describe Scusi?, a multi-stage, spoken language interpretation mechanism designed to be part of a robot- mounted dialogue system. Scusi?'s interpretation process maps spoken utterances to conceptual graphs, and the nodes in these graphs to concepts in the world. Maximum posterior probability is used to rank the (partial) interpretations produced at each stage of this process. We show how the features of our interpretation process yield desirable behaviours that support robust and flexible system performance.