Consensus Network Decoding for Statistical Machine Translation System Combination

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 transla...

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Bibliographic Details
Main Authors: Sim, K. C., Byrne, W. J., Gales, M. J. F., Sahbi, H., Woodland, P. C.
Format: Conference Proceeding
Language:English
Subjects:
consensus decoding
Decoding
Erbium
Error analysis
Machine translation
Minimum Bayes Risk (MBR) decoding
Natural languages
NIST
Robustness
Speech recognition
Subcontracting
system combination
US Government
Voting
Online Access:Request full text
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Description
Summary: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.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2007.367174