@inproceedings{popovic-ney-2006-pos,
title = "{POS}-based Word Reorderings for Statistical Machine Translation",
author = "Popovi{\'c}, Maja and
Ney, Hermann",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Gangemi, Aldo and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Tapias, Daniel",
booktitle = "Proceedings of the Fifth International Conference on Language Resources and Evaluation ({LREC}{'}06)",
month = may,
year = "2006",
address = "Genoa, Italy",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2006/pdf/412_pdf.pdf",
abstract = "Translation In this work we investigate new possibilities for improving the quality of statistical machine translation (SMT) by applying word reorderings of the source language sentences based on Part-of-Speech tags. Results are presented on the European Parliament corpus containing about 700k sentences and 15M running words. In order to investigate sparse training data scenarios, we also report results obtained on about 1{\textbackslash}{\%} of the original corpus. The source languages are Spanish and English and target languages are Spanish, English and German. We propose two types of reorderings depending on the language pair and the translation direction: local reorderings of nouns and adjectives for translation from and into Spanish and long-range reorderings of verbs for translation into German. For our best translation system, we achieve up to 2{\textbackslash}{\%} relative reduction of WER and up to 7{\textbackslash}{\%} relative increase of BLEU score. Improvements can be seen both on the reordered sentences as well as on the rest of the test corpus. Local reorderings are especially important for the translation systems trained on the small corpus whereas long-range reorderings are more effective for the larger corpus.",
}
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<abstract>Translation In this work we investigate new possibilities for improving the quality of statistical machine translation (SMT) by applying word reorderings of the source language sentences based on Part-of-Speech tags. Results are presented on the European Parliament corpus containing about 700k sentences and 15M running words. In order to investigate sparse training data scenarios, we also report results obtained on about 1\textbackslash% of the original corpus. The source languages are Spanish and English and target languages are Spanish, English and German. We propose two types of reorderings depending on the language pair and the translation direction: local reorderings of nouns and adjectives for translation from and into Spanish and long-range reorderings of verbs for translation into German. For our best translation system, we achieve up to 2\textbackslash% relative reduction of WER and up to 7\textbackslash% relative increase of BLEU score. Improvements can be seen both on the reordered sentences as well as on the rest of the test corpus. Local reorderings are especially important for the translation systems trained on the small corpus whereas long-range reorderings are more effective for the larger corpus.</abstract>
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%0 Conference Proceedings
%T POS-based Word Reorderings for Statistical Machine Translation
%A Popović, Maja
%A Ney, Hermann
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Gangemi, Aldo
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Tapias, Daniel
%S Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)
%D 2006
%8 May
%I European Language Resources Association (ELRA)
%C Genoa, Italy
%F popovic-ney-2006-pos
%X Translation In this work we investigate new possibilities for improving the quality of statistical machine translation (SMT) by applying word reorderings of the source language sentences based on Part-of-Speech tags. Results are presented on the European Parliament corpus containing about 700k sentences and 15M running words. In order to investigate sparse training data scenarios, we also report results obtained on about 1\textbackslash% of the original corpus. The source languages are Spanish and English and target languages are Spanish, English and German. We propose two types of reorderings depending on the language pair and the translation direction: local reorderings of nouns and adjectives for translation from and into Spanish and long-range reorderings of verbs for translation into German. For our best translation system, we achieve up to 2\textbackslash% relative reduction of WER and up to 7\textbackslash% relative increase of BLEU score. Improvements can be seen both on the reordered sentences as well as on the rest of the test corpus. Local reorderings are especially important for the translation systems trained on the small corpus whereas long-range reorderings are more effective for the larger corpus.
%U http://www.lrec-conf.org/proceedings/lrec2006/pdf/412_pdf.pdf
Markdown (Informal)
[POS-based Word Reorderings for Statistical Machine Translation](http://www.lrec-conf.org/proceedings/lrec2006/pdf/412_pdf.pdf) (Popović & Ney, LREC 2006)
ACL