Multi-Engine Machine Translation by Recursive Sentence Decomposition

Bart Mellebeek, Karolina Owczarzak, Josef Van Genabith, Andy Way


Abstract
In this paper, we present a novel approach to combine the outputs of multiple MT engines into a consensus translation. In contrast to previous Multi-Engine Machine Translation (MEMT) techniques, we do not rely on word alignments of output hypotheses, but prepare the input sentence for multi-engine processing. We do this by using a recursive decomposition algorithm that produces simple chunks as input to the MT engines. A consensus translation is produced by combining the best chunk translations, selected through majority voting, a trigram language model score and a confidence score assigned to each MT engine. We report statistically significant relative improvements of up to 9% BLEU score in experiments (English→Spanish) carried out on an 800-sentence test set extracted from the Penn-II Treebank.
Anthology ID:
2006.amta-papers.13
Volume:
Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers
Month:
August 8-12
Year:
2006
Address:
Cambridge, Massachusetts, USA
Venue:
AMTA
SIG:
Publisher:
Association for Machine Translation in the Americas
Note:
Pages:
110–118
Language:
URL:
https://aclanthology.org/2006.amta-papers.13
DOI:
Bibkey:
Cite (ACL):
Bart Mellebeek, Karolina Owczarzak, Josef Van Genabith, and Andy Way. 2006. Multi-Engine Machine Translation by Recursive Sentence Decomposition. In Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pages 110–118, Cambridge, Massachusetts, USA. Association for Machine Translation in the Americas.
Cite (Informal):
Multi-Engine Machine Translation by Recursive Sentence Decomposition (Mellebeek et al., AMTA 2006)
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PDF:
https://preview.aclanthology.org/update-css-js/2006.amta-papers.13.pdf