System Combination for Machine Translation of Spoken and Written Language

This paper describes an approach for computing a consensus translation from the outputs of multiple machine translation (MT) systems. The consensus translation is computed by weighted majority voting on a confusion network, similarly to the well-established ROVER approach of Fiscus for combining speech recognition hypotheses. To create the confusion network, pairwise word alignments of the original MT hypotheses are learned using an enhanced statistical alignment algorithm that explicitly models word reordering. The context of a whole corpus of automatic translations rather than a single sentence is taken into account in order to achieve high alignment quality. The confusion network is rescored with a special language model, and the consensus translation is extracted as the best path. The proposed system combination approach was evaluated in the framework of the TC-STAR speech translation project. Up to six state-of-the-art statistical phrase-based translation systems from different pr...

• Type: Journal
• Year: 2008
• Where: TASLP
• Authors: Evgeny Matusov, Gregor Leusch, Rafael E. Banchs, Nicola Bertoldi, Daniel Dechelotte, Marcello Federico, M. Kolss, Young-Suk Lee, José B. Mariño, M. Paulik, Salim Roukos, Holger Schwenk, Hermann Ney
• Keywords: Confusion Network, Consensus Translation, Multiple Machine Translation, TASLP 2008