A Source-side Decoding Sequence Model for Statistical Machine Translation

Minwei Feng, Arne Mauser, Hermann Ney


Abstract
We propose a source-side decoding sequence language model for phrase-based statistical machine translation. This model is a reordering model in the sense that it helps the decoder find the correct decoding sequence. The model uses word-aligned bilingual training data. We show improved translation quality of up to 1.34% BLEU and 0.54% TER using this model compared to three other widely used reordering models.
Anthology ID:
2010.amta-papers.22
Volume:
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers
Month:
October 31-November 4
Year:
2010
Address:
Denver, Colorado, USA
Venue:
AMTA
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Publisher:
Association for Machine Translation in the Americas
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URL:
https://aclanthology.org/2010.amta-papers.22
DOI:
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Cite (ACL):
Minwei Feng, Arne Mauser, and Hermann Ney. 2010. A Source-side Decoding Sequence Model for Statistical Machine Translation. In Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers, Denver, Colorado, USA. Association for Machine Translation in the Americas.
Cite (Informal):
A Source-side Decoding Sequence Model for Statistical Machine Translation (Feng et al., AMTA 2010)
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PDF:
https://aclanthology.org/2010.amta-papers.22.pdf