  We describe an implementation of a hybrid statistical/symbolic approach to
repairing parser failures in a speech-to-speech translation system. We describe
a module which takes as input a fragmented parse and returns a repaired meaning
representation. It negotiates with the speaker about what the complete meaning
of the utterance is by generating hypotheses about how to fit the fragments of
the partial parse together into a coherent meaning representation. By drawing
upon both statistical and symbolic information, it constrains its repair
hypotheses to those which are both likely and meaningful. Because it updates
its statistical model during use, it improves its performance over time.
