Introduction to algorithms
Three heads are better than one
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
Ambiguity preserving machine translation using packed representations
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Multi-engine machine translation guided by explicit word matching
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
Multi-engine machine translation with an open-source decoder for statistical machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
First steps towards multi-engine machine translation
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
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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.