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We present an approach to dialogue management and interpretation that evaluates and selects amongst candidate dialogue moves based on features at multiple levels. Multiple interpretation methods can be combined, multiple speech recognition and parsing hypotheses tested, and multiple candidate dialogue moves considered to choose the highest scoring hypothesis overall. We integrate hypotheses generated from shallow slot-filling methods and from relatively deep parsing, using pragmatic information. We show that this gives more robust performance than using either approach alone, allowing n-best list reordering to correct errors in speech recognition or parsing.