Learning to Select a Good Translation

Within the machine translation system Verbmobil, translation is performed simultaneously by four independent translation modules. The four competing translations are combined by a selection module so as to form a single optimal output for each input utterance. The selection module relies on con dence values that are delivered together with each of the alternative translations. Since the con dence values are computed by four independent modules that are fundamentally di erent from one another, they are not directly comparable and need to be rescaled in order to gain comparative signi cance. In this paper we describe a machine learning method tailored to overcome this diÆculty by using o -line human feedback to determine an appropriate con dence rescaling scheme. Additionally, we describe some other sources of information that are used for selecting between the competing translations, and describe the way in which the selection process relates to quality of service speci cations.