research-article

Syntax-Based Post-Ordering for Efficient Japanese-to-English Translation

Authors:

Katsuhito Sudoh,

Hajime Tsukada,

Masaaki NagataAuthors Info & Claims

ACM Transactions on Asian Language Information Processing (TALIP), Volume 12, Issue 3

Article No.: 12, Pages 1 - 15

https://doi.org/10.1145/2499955.2499960

Published: 01 August 2013 Publication History

Abstract

This article proposes a novel reordering method for efficient two-step Japanese-to-English statistical machine translation (SMT) that isolates reordering from SMT and solves it after lexical translation. This reordering problem, called post-ordering, is solved as an SMT problem from Head-Final English (HFE) to English. HFE is syntax-based reordered English that is very successfully used for reordering with English-to-Japanese SMT. The proposed method incorporates its advantage into the reverse direction, Japanese-to-English, and solves the post-ordering problem by accurate syntax-based SMT with target language syntax. Two-step SMT with the proposed post-ordering empirically reduces the decoding time of the accurate but slow syntax-based SMT by its good approximation using intermediate HFE. The proposed method improves the decoding speed of syntax-based SMT decoding by about six times with comparable translation accuracy in Japanese-to-English patent translation experiments.

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Cited By

Anirudh CKavi N(2023)How Good are Transformers in Reordering?Multi-disciplinary Trends in Artificial Intelligence10.1007/978-3-031-36402-0_5(60-67)Online publication date: 24-Jun-2023
https://doi.org/10.1007/978-3-031-36402-0_5
Farzi SFaili HKianian S(2018)A neural reordering model based on phrasal dependency tree for statistical machine translationIntelligent Data Analysis10.3233/IDA-17358222:5(1163-1183)Online publication date: 26-Sep-2018
https://doi.org/10.3233/IDA-173582
Farzi SFaili HKianian S(2018)A preordering model based on phrasal dependency treeDigital Scholarship in the Humanities10.1093/llc/fqy00933:4(748-765)Online publication date: 18-May-2018
https://doi.org/10.1093/llc/fqy009
Show More Cited By

Index Terms

Syntax-Based Post-Ordering for Efficient Japanese-to-English Translation
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Machine translation

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Published In

cover image ACM Transactions on Asian Language Information Processing

ACM Transactions on Asian Language Information Processing Volume 12, Issue 3

August 2013

76 pages

ISSN:1530-0226

EISSN:1558-3430

DOI:10.1145/2499955

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Copyright © 2013 ACM.

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Association for Computing Machinery

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Publication History

Published: 01 August 2013

Accepted: 01 December 2012

Revised: 01 November 2012

Received: 01 February 2012

Published in TALIP Volume 12, Issue 3

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Cited By

Anirudh CKavi N(2023)How Good are Transformers in Reordering?Multi-disciplinary Trends in Artificial Intelligence10.1007/978-3-031-36402-0_5(60-67)Online publication date: 24-Jun-2023
https://doi.org/10.1007/978-3-031-36402-0_5
Farzi SFaili HKianian S(2018)A neural reordering model based on phrasal dependency tree for statistical machine translationIntelligent Data Analysis10.3233/IDA-17358222:5(1163-1183)Online publication date: 26-Sep-2018
https://doi.org/10.3233/IDA-173582
Farzi SFaili HKianian S(2018)A preordering model based on phrasal dependency treeDigital Scholarship in the Humanities10.1093/llc/fqy00933:4(748-765)Online publication date: 18-May-2018
https://doi.org/10.1093/llc/fqy009
Bisazza AFederico M(2016)A survey of word reordering in statistical machine translationComputational Linguistics10.1162/COLI_a_0024542:2(163-205)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1162/COLI_a_00245
Ding CSakanushi KTouji HYamamoto M(2016)Inter-, Intra-, and Extra-Chunk Pre-Ordering for Statistical Japanese-to-English Machine TranslationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/281838115:3(1-28)Online publication date: 9-Jan-2016
https://dl.acm.org/doi/10.1145/2818381
Farzi SFaili H(2015)Improving Statistical Machine Translation using Syntax-based Learning-to-Rank SystemDigital Scholarship in the Humanities10.1093/llc/fqv032(fqv032)Online publication date: 12-Aug-2015
https://doi.org/10.1093/llc/fqv032

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