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Integration of Multiple Bilingually-Trained Segmentation Schemes into Statistical Machine Translation

Michael PAUL, Andrew FINCH, Eiichiro SUMITA

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Summary :

This paper proposes an unsupervised word segmentation algorithm that identifies word boundaries in continuous source language text in order to improve the translation quality of statistical machine translation (SMT) approaches. The method can be applied to any language pair in which the source language is unsegmented and the target language segmentation is known. In the first step, an iterative bootstrap method is applied to learn multiple segmentation schemes that are consistent with the phrasal segmentations of an SMT system trained on the resegmented bitext. In the second step, multiple segmentation schemes are integrated into a single SMT system by characterizing the source language side and merging identical translation pairs of differently segmented SMT models. Experimental results translating five Asian languages into English revealed that the proposed method of integrating multiple segmentation schemes outperforms SMT models trained on any of the learned word segmentations and performs comparably to available monolingually built segmentation tools.

Publication
IEICE TRANSACTIONS on Information Vol.E94-D No.3 pp.690-697
Publication Date
2011/03/01
Publicized
Online ISSN
1745-1361
DOI
10.1587/transinf.E94.D.690
Type of Manuscript
PAPER
Category
Natural Language Processing

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