Computing Consensus Translation from Multiple Machine Translation Systems Using Enhanced Hypotheses Alignment (2006)
| Venue: | Cambridge University Engineering Department |
| Citations: | 26 - 5 self |
BibTeX
@INPROCEEDINGS{Matusov06computingconsensus,
author = {Evgeny Matusov and Nicola Ueffing and Hermann Ney},
title = {Computing Consensus Translation from Multiple Machine Translation Systems Using Enhanced Hypotheses Alignment},
booktitle = {Cambridge University Engineering Department},
year = {2006},
pages = {33--40}
}
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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.
