IEICE TRANSACTIONS on Information
A Reordering Model Using a Source-Side Parse-Tree for Statistical Machine Translation
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
Authors
Keyword
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Kei HASHIMOTO, Hirofumi YAMAMOTO, Hideo OKUMA, Eiichiro SUMITA, Keiichi TOKUDA, "A Reordering Model Using a Source-Side Parse-Tree for Statistical Machine Translation" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 12, pp. 2386-2393, December 2009, doi: 10.1587/transinf.E92.D.2386.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.2386/_p
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@ARTICLE{e92-d_12_2386,
author={Kei HASHIMOTO, Hirofumi YAMAMOTO, Hideo OKUMA, Eiichiro SUMITA, Keiichi TOKUDA, },
journal={IEICE TRANSACTIONS on Information},
title={A Reordering Model Using a Source-Side Parse-Tree for Statistical Machine Translation},
year={2009},
volume={E92-D},
number={12},
pages={2386-2393},
abstract={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.},
keywords={},
doi={10.1587/transinf.E92.D.2386},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - A Reordering Model Using a Source-Side Parse-Tree for Statistical Machine Translation
T2 - IEICE TRANSACTIONS on Information
SP - 2386
EP - 2393
AU - Kei HASHIMOTO
AU - Hirofumi YAMAMOTO
AU - Hideo OKUMA
AU - Eiichiro SUMITA
AU - Keiichi TOKUDA
PY - 2009
DO - 10.1587/transinf.E92.D.2386
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2009
AB - 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.
ER -
English
