Learning to select a good translation
Dan Tidhar, Uwe Küssner · 2000
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 confidence values that are delivered together with each of the alternative translations. Since the confidence values are computed by four independent modules that are fundamentally different from one another, they are not directly comparable and need to be rescaled in order to gain comparative significance. In this paper we describe a machine learning method tailored to overcome this difficulty by using off-line human feedback to determine an appropriate confidence 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 specifications.