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How to Choose the Best Pivot Language for Automatic Translation of Low-Resource Languages

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Eiichrio SumitaAuthors Info & Claims

ACM Transactions on Asian Language Information Processing (TALIP), Volume 12, Issue 4

Article No.: 14, Pages 1 - 17

https://doi.org/10.1145/2505126

Published: 01 October 2013 Publication History

Abstract

Recent research on multilingual statistical machine translation focuses on the usage of pivot languages in order to overcome language resource limitations for certain language pairs. Due to the richness of available language resources, English is, in general, the pivot language of choice. However, factors like language relatedness can also effect the choice of the pivot language for a given language pair, especially for Asian languages, where language resources are currently quite limited. In this article, we provide new insights into what factors make a pivot language effective and investigate the impact of these factors on the overall pivot translation performance for translation between 22 Indo-European and Asian languages. Experimental results using state-of-the-art statistical machine translation techniques revealed that the translation quality of 54.8% of the language pairs improved when a non-English pivot language was chosen. Moreover, 81.0% of system performance variations can be explained by a combination of factors such as language family, vocabulary, sentence length, language perplexity, translation model entropy, reordering, monotonicity, and engine performance.

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Cited By

Pei JYan Gde Rijke MRen P(2024)Mixture-of-languages Routing for Multilingual DialoguesACM Transactions on Information Systems10.1145/3676956Online publication date: 5-Aug-2024
https://doi.org/10.1145/3676956
Agrawal ASingh LJacobs ELiu YDunlevy GPokharel RUppala V(2023)All Translation Tools Are Not Equal: Investigating the Quality of Language Translation for Forced Migration2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA60987.2023.10302481(1-10)Online publication date: 9-Oct-2023
https://doi.org/10.1109/DSAA60987.2023.10302481
Wang LLi J(2023)Research on Chinese-Lao Neural Machine Translation Based on Multi-Pivot2023 2nd International Conference on Artificial Intelligence and Computer Information Technology (AICIT)10.1109/AICIT59054.2023.10277799(1-5)Online publication date: 15-Sep-2023
https://doi.org/10.1109/AICIT59054.2023.10277799
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Index Terms

How to Choose the Best Pivot Language for Automatic Translation of Low-Resource Languages
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 4

October 2013

86 pages

ISSN:1530-0226

EISSN:1558-3430

DOI:10.1145/2523057

Issue’s Table of Contents

Copyright © 2013 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 October 2013

Accepted: 01 July 2013

Revised: 01 May 2013

Received: 01 January 2013

Published in TALIP Volume 12, Issue 4

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Cited By

Pei JYan Gde Rijke MRen P(2024)Mixture-of-languages Routing for Multilingual DialoguesACM Transactions on Information Systems10.1145/3676956Online publication date: 5-Aug-2024
https://doi.org/10.1145/3676956
Agrawal ASingh LJacobs ELiu YDunlevy GPokharel RUppala V(2023)All Translation Tools Are Not Equal: Investigating the Quality of Language Translation for Forced Migration2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA60987.2023.10302481(1-10)Online publication date: 9-Oct-2023
https://doi.org/10.1109/DSAA60987.2023.10302481
Wang LLi J(2023)Research on Chinese-Lao Neural Machine Translation Based on Multi-Pivot2023 2nd International Conference on Artificial Intelligence and Computer Information Technology (AICIT)10.1109/AICIT59054.2023.10277799(1-5)Online publication date: 15-Sep-2023
https://doi.org/10.1109/AICIT59054.2023.10277799
Shi SWu XSu RHuang H(2022)Low-resource Neural Machine Translation: Methods and TrendsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/352430021:5(1-22)Online publication date: 15-Mar-2022
https://dl.acm.org/doi/10.1145/3524300
Budiwati SAritsugi M(2021)Word reordering on multiple pivots for the Japanese and Indonesian language pairMachine Translation10.1007/s10590-021-09288-835:4(611-636)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1007/s10590-021-09288-8
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Song YUpadhyay SPeng HMayhew SRoth D(2019)Toward any-language zero-shot topic classification of textual documentsArtificial Intelligence10.1016/j.artint.2019.02.002Online publication date: Feb-2019
https://doi.org/10.1016/j.artint.2019.02.002
Zhu XYang MZhao TZhu C(2018)Minimum Bayes-Risk Phrase Table Pruning for Pivot-Based Machine Translation in Internet of ThingsIEEE Access10.1109/ACCESS.2018.28727736(55754-55764)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2872773
Ruggeri ASamoggia A(2017)Twitter communication of agri‐food chain actors on palm oil environmental, socio‐economic, and health sustainabilityJournal of Consumer Behaviour10.1002/cb.169917:1(75-93)Online publication date: 12-Dec-2017
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