tailieunhanh - Báo cáo khoa học: "Computing Consensus Translation from Multiple Machine Translation Systems Using Enhanced Hypotheses Alignment"

This paper describes a novel method for computing a consensus translation from the outputs of multiple machine translation (MT) systems. The outputs are combined and a possibly new translation hypothesis can be generated. Similarly to the well-established ROVER approach of (Fiscus, 1997) for combining speech recognition hypotheses, the consensus translation is computed by voting on a confusion network. To create the confusion network, we produce pairwise word alignments of the original machine translation hypotheses with an enhanced statistical alignment algorithm that explicitly models word reordering. . | Computing Consensus Translation from Multiple Machine Translation Systems Using Enhanced Hypotheses Alignment Evgeny Matusov Nicola Ueffing Hermann Ney Lehrstuhl fur Informatik VI - Computer Science Department RWTH Aachen University Aachen Germany. matusov ueffing ney @ Abstract This paper describes a novel method for computing a consensus translation from the outputs of multiple machine translation MT systems. The outputs are combined and a possibly new translation hypothesis can be generated. Similarly to the well-established ROVER approach of Fiscus 1997 for combining speech recognition hypotheses the consensus translation is computed by voting on a confusion network. To create the confusion network we produce pairwise word alignments of the original machine translation hypotheses with an enhanced statistical alignment algorithm that explicitly models word reordering. The context of a whole document of translations rather than a single sentence is taken into account to produce the alignment. The proposed alignment and voting approach was evaluated on several machine translation tasks including a large vocabulary task. The method was also tested in the framework of multi-source and speech translation. On all tasks and conditions we achieved significant improvements in translation quality increasing e. g. the BLEU score by as much as 15 relative. 1 Introduction In this work we describe a novel technique for computing a consensus translation from the outputs of multiple machine translation systems. Combining outputs from different systems was shown to be quite successful in automatic speech recognition ASR . Voting schemes like the ROVER approach of Fiscus 1997 use edit distance alignment and time information to create confusion networks from the output of several ASR systems. Some research on multi-engine machine translation has also been performed in recent years. The most straightforward approaches simply select for each sentence one of