CONSENSUS NETWORK DECODING FOR STATISTICAL MACHINE TRANSLATION SYSTEM COMBINATION

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“CONSENSUS NETWORK DECODING FOR STATISTICAL MACHINE TRANSLATION SYSTEM COMBINATION” by K.-C. Sim, W. Byrne, M. Gales, H. Sahbi, and P.C. Woodland. In IEEE Conference on Acoustics, Speech and Signal Processing, 2007.

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.

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BibTeX entry:

@inproceedings{icassp07:cnsmt,
   author = {K.-C. Sim and W. Byrne and M. Gales and H. Sahbi and P.C.
	Woodland},
   title = {CONSENSUS NETWORK DECODING FOR STATISTICAL MACHINE TRANSLATION
	SYSTEM COMBINATION},
   booktitle = {IEEE Conference on Acoustics, Speech and Signal Processing},
   pages = {(4 pages)},
   year = {2007}
}

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