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Rule-Based Machine Translation for the Automatic Translation of Vietnamese Sign Language

Received: 6 November 2023     Accepted: 8 December 2023     Published: 18 December 2023
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Abstract

Sign language is acknowledged as a unique language in the field of machine translation, possessing distinct grammatical characteristics compared to written or spoken Vietnamese. These include simplifications, altered word order, and emphasis on stress. This article explores a rule-based machine translation approach specifically designed to translate Vietnamese utterances into grammatically accurate Vietnamese Sign Language sentences. While considered a conventional technique, this approach demonstrates remarkable success in this specific scenario. Evaluation results reveal that the proposed method outperforms several contemporary machine translation models for this particular challenge, achieving a BLEU score of 62.55. This achievement is particularly noteworthy considering the limited resources available for Vietnamese Sign Language. Moreover, experiments conducted with varying data sizes further solidify the effectiveness of this method within a defined domain. Notably, the BLEU score surpasses expectations for typical translation problems, highlighting the effectiveness of both the probabilistic model and the intuitive linguistic model employed. This study demonstrates the potential of rule-based machine translation for Vietnamese Sign Language, particularly in situations where resources are limited. The encouraging results pave the way for further research and development in this area, ultimately aiming to improve communication and accessibility for the Vietnamese deaf community.

Published in International Journal of Language and Linguistics (Volume 11, Issue 6)
DOI 10.11648/j.ijll.20231106.12
Page(s) 191-198
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Natural Language Processing, Machine Translation, Rule-Based, Low-Resource Language, Sign Language

References
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[2] S Camgoz Necati, K. O., Hadfield Simon, Bowden Richard, Sign Language Transformers: Joint End-to-End Sign Language Recognition and Translation, ed. 10.1109/CVPR42600.2020.01004. 2020.
[3] Son-Thai Le, N.-T. D., Van-Thu Ma, Thi-Bich-Diep Nguyen, A technique to control human movements in virtual reality using Vietnamese sign language visualization, in Fundamental And Applied IT Research. 2017.
[4] Stephen Cox, M. L., Judy Tryggvason, Melanie Nakisa, Mark Wells, Marcus Tutt. Tessa, a system to aid communication with deaf people. in The fifth international ACM conference on Assistive technologies. 2002.
[5] J. A. Bangham, S. J. C., R. Elliot, J. R. W. Glauert, I. Marshall, S. Rankov, and M. Wells, Virtual signing: Capture, animation, storage and transmission – An overview of the ViSiCAST project. IEEE Seminar on Speech and language processing for disabled and elderly people, 2000.
[6] SignSynth: A Sign Language Synthesis Application Using Web3D and Perl. in Revised Papers from the International Gesture Workshop on Gesture and Sign Languages in Human-Computer Interaction. 2002.
[7] Bernd Krieg-Brückner, J. P., Ernst-Rüdiger Olderog, Alexander Baer, The Uniform Workbench, a Universal Development Environment for Formal Method, ed. L. N. i. C. Science. 1999: Springer.
[8] Gouri Sankar Mishra, A. K. S. a. K. K. R., Word based statistical machine translation from english text to indian sign language. ARPN Journal of Engineering and Applied Sciences, 2017. 12.
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[10] Gouri Sankar Mishra, Ashok Kumar Sahoo and Kiran Kumar Ravulakollu, “Word based statistical machine translation from english text to Indian sign language”, ARPN Journal of Engineering and Applied Sciences, VOL. 12, NO. 2, 2017.
[11] Galina Angelova, Eleftherios Avramidis and Sebastian Möller, Using Neural Machine Translation Methods for Sign Language Translation, 60th Annual Meeting of the Association for Computational Linguistics Student Research Workshop, pages 273 – 284, 2022.
[12] Kacorri, H., Huenerfauth, M., Ebling, S., Patel, K., Menzies, K., and Willard, M., Regression Analysis of Demographic and Technology-Experience Factors Influencing Acceptance of Sign Language Animation, ACM Transactions on Accessible Computing, 10(1): 1–33, 2017.
[13] Bragg, D., Kacorri, H., et al, Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective, In The 21st International ACM SIGACCESS Conference on Computers and Accessibility - ASSETS’19, pages 16–31, Pittsburgh, PA, USA. ACM Press, 2019.
[14] Ilias Papastratis, Chatzikontantinou Christos, Dimitrios Konstantinidis, Kosmas Dimitropoulos, Petros Daras, Automated Sign Language Translation: The Role of Artificial Intelligence Now and in the Future, In Proceedings of the 4th International Conference on Computer-Human Interaction Research and Applications, pages 170-177, 2020.
[15] De Martino, et al, Signing avatars: Making education more inclusive, Universal Access in the Information Society, 16(3): 793–808, 2017.
[16] Razieh Rastgoo, Kourosh Kiani, Sergio Escalera, Vassilis Athitsos, Mohammad Sabokrou, All You Need In Sign Language, Production, arXiv: 2201.01609v2 [cs.CV] 6 Jan 2022.
[17] Kayo Yin, J. R. Better Sign Language Translation with STMC-Transformer. in The 28th International Conference on Computational Linguistics. 2020.
[18] Abercrombie, G. A rule-based shallow-transfer machine translation system for Scots and English. in Conference on International Language Resources and Evaluation (LREC'16). 2016.
[19] Thi-Hien Do, Sign language for deaf community in Vietnam: Problems and Solutions, Project report. Social Science Academy of Vietnam, 2012: p. 26.
[20] Thi-Bich-Diep Nguyen, T.-N. P., Some issues on syntax transformation in Vietnamese sign language translation. Sign Language Studies. IJCSNS International Journal of Computer Science and Network Security, 2017. 17.
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Cite This Article
  • APA Style

    Nguyen, T., Nguyen, T. (2023). Rule-Based Machine Translation for the Automatic Translation of Vietnamese Sign Language. International Journal of Language and Linguistics, 11(6), 191-198. https://doi.org/10.11648/j.ijll.20231106.12

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    ACS Style

    Nguyen, T.; Nguyen, T. Rule-Based Machine Translation for the Automatic Translation of Vietnamese Sign Language. Int. J. Lang. Linguist. 2023, 11(6), 191-198. doi: 10.11648/j.ijll.20231106.12

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    AMA Style

    Nguyen T, Nguyen T. Rule-Based Machine Translation for the Automatic Translation of Vietnamese Sign Language. Int J Lang Linguist. 2023;11(6):191-198. doi: 10.11648/j.ijll.20231106.12

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  • @article{10.11648/j.ijll.20231106.12,
      author = {Thi-Bich-Diep Nguyen and Thi-Tam Nguyen},
      title = {Rule-Based Machine Translation for the Automatic Translation of Vietnamese Sign Language},
      journal = {International Journal of Language and Linguistics},
      volume = {11},
      number = {6},
      pages = {191-198},
      doi = {10.11648/j.ijll.20231106.12},
      url = {https://doi.org/10.11648/j.ijll.20231106.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijll.20231106.12},
      abstract = {Sign language is acknowledged as a unique language in the field of machine translation, possessing distinct grammatical characteristics compared to written or spoken Vietnamese. These include simplifications, altered word order, and emphasis on stress. This article explores a rule-based machine translation approach specifically designed to translate Vietnamese utterances into grammatically accurate Vietnamese Sign Language sentences. While considered a conventional technique, this approach demonstrates remarkable success in this specific scenario. Evaluation results reveal that the proposed method outperforms several contemporary machine translation models for this particular challenge, achieving a BLEU score of 62.55. This achievement is particularly noteworthy considering the limited resources available for Vietnamese Sign Language. Moreover, experiments conducted with varying data sizes further solidify the effectiveness of this method within a defined domain. Notably, the BLEU score surpasses expectations for typical translation problems, highlighting the effectiveness of both the probabilistic model and the intuitive linguistic model employed. This study demonstrates the potential of rule-based machine translation for Vietnamese Sign Language, particularly in situations where resources are limited. The encouraging results pave the way for further research and development in this area, ultimately aiming to improve communication and accessibility for the Vietnamese deaf community.
    },
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - Rule-Based Machine Translation for the Automatic Translation of Vietnamese Sign Language
    AU  - Thi-Bich-Diep Nguyen
    AU  - Thi-Tam Nguyen
    Y1  - 2023/12/18
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    N1  - https://doi.org/10.11648/j.ijll.20231106.12
    DO  - 10.11648/j.ijll.20231106.12
    T2  - International Journal of Language and Linguistics
    JF  - International Journal of Language and Linguistics
    JO  - International Journal of Language and Linguistics
    SP  - 191
    EP  - 198
    PB  - Science Publishing Group
    SN  - 2330-0221
    UR  - https://doi.org/10.11648/j.ijll.20231106.12
    AB  - Sign language is acknowledged as a unique language in the field of machine translation, possessing distinct grammatical characteristics compared to written or spoken Vietnamese. These include simplifications, altered word order, and emphasis on stress. This article explores a rule-based machine translation approach specifically designed to translate Vietnamese utterances into grammatically accurate Vietnamese Sign Language sentences. While considered a conventional technique, this approach demonstrates remarkable success in this specific scenario. Evaluation results reveal that the proposed method outperforms several contemporary machine translation models for this particular challenge, achieving a BLEU score of 62.55. This achievement is particularly noteworthy considering the limited resources available for Vietnamese Sign Language. Moreover, experiments conducted with varying data sizes further solidify the effectiveness of this method within a defined domain. Notably, the BLEU score surpasses expectations for typical translation problems, highlighting the effectiveness of both the probabilistic model and the intuitive linguistic model employed. This study demonstrates the potential of rule-based machine translation for Vietnamese Sign Language, particularly in situations where resources are limited. The encouraging results pave the way for further research and development in this area, ultimately aiming to improve communication and accessibility for the Vietnamese deaf community.
    
    VL  - 11
    IS  - 6
    ER  - 

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Author Information
  • Department of Information Technology, Thai Nguyen University of Information and Communication Technology (ICTU), Thai Nguyen, Vietnam

  • Department of Electronics and Informatics, College of Industrial Techniques (CIT), Bac Giang, Vietnam

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