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A Reordering Model Using a Source-Side Parse-Tree for Statistical Machine Translation

Kei HASHIMOTO, Hirofumi YAMAMOTO, Hideo OKUMA, Eiichiro SUMITA, Keiichi TOKUDA

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Summary :

This paper presents a reordering model using a source-side parse-tree for phrase-based statistical machine translation. The proposed model is an extension of IST-ITG (imposing source tree on inversion transduction grammar) constraints. In the proposed method, the target-side word order is obtained by rotating nodes of the source-side parse-tree. We modeled the node rotation, monotone or swap, using word alignments based on a training parallel corpus and source-side parse-trees. The model efficiently suppresses erroneous target word orderings, especially global orderings. Furthermore, the proposed method conducts a probabilistic evaluation of target word reorderings. In English-to-Japanese and English-to-Chinese translation experiments, the proposed method resulted in a 0.49-point improvement (29.31 to 29.80) and a 0.33-point improvement (18.60 to 18.93) in word BLEU-4 compared with IST-ITG constraints, respectively. This indicates the validity of the proposed reordering model.

Publication
IEICE TRANSACTIONS on Information Vol.E92-D No.12 pp.2386-2393
Publication Date
2009/12/01
Publicized
Online ISSN
1745-1361
DOI
10.1587/transinf.E92.D.2386
Type of Manuscript
Special Section PAPER (Special Section on Natural Language Processing and its Applications)
Category
Machine Translation

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