The paper argues the importance of high-quality translation for spoken language translation systems. It describes an architecture suitable for rapid development of high-quality limited-domain translation systems, which has been implemented within an advanced prototype English to French spoken language translator. The focus of the paper is the hybrid transfer model which combines unification-based rules and a set of trainable statistical preferences; roughly, rules encode domain-independent grammatical information and preferences encode domain-dependent distributional information. The preferences are trained from sets of examples produced by the system, which have been annotated by human judges as correct or incorrect. An experiment is described in which the model was tested on a 2000 utterance sample of previously unseen data.