DMCA
Consensus network decoding for statistical machine translation system combination (2007)
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| Venue: | In ICASSP |
| Citations: | 48 - 9 self |
BibTeX
@INPROCEEDINGS{Sim07consensusnetwork,
author = {K. C. Sim and W. J. Byrne and M. J. F. Gales and H. Sahbi and P. C. Woodl},
title = {Consensus network decoding for statistical machine translation system combination},
booktitle = {In ICASSP},
year = {2007}
}
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Abstract
This paper presents a simple and robust consensus decoding approach for combining multiple Machine Translation (MT) system outputs. A consensus network is constructed from an N-best list by aligning the hypotheses against an alignment reference, where the alignment is based on minimising the translation edit rate (TER). The Minimum Bayes Risk (MBR) decoding technique is investigated for the selection of an appropriate alignment reference. Several alternative decoding strategies proposed to retain coherent phrases in the original translations. Experimental results are presented primarily based on three-way combination of Chinese-English translation outputs, and also presents results for six-way system combination. It is shown that worthwhile improvements in translation performance can be obtained using the methods discussed. Index Terms — Machine translation, system combination, consensus decoding, Minimum Bayes Risk (MBR) decoding
Keyphrases
consensus network statistical machine translation system combination minimum bayes risk n-best list three-way combination system combination experimental result six-way system combination alignment reference original translation index term machine translation consensus decoding worthwhile improvement robust consensus translation edit rate appropriate alignment reference chinese-english translation output multiple machine translation decoding technique translation performance coherent phrase
