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Sustainability and Influence of Machine Translation: Perceptions and Attitudes of Translation Instructors and Learners in Hong Kong

Author

Listed:
  • Kanglong Liu

    (Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China)

  • Ho Ling Kwok

    (Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China)

  • Jianwen Liu

    (Department of English Language and Literature, Hong Kong Shue Yan University, Hong Kong, China)

  • Andrew K.F. Cheung

    (Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China)

Abstract

In this era of globalisation, translation technologies have become more popular in daily communication, the education sector, and the translation industry. It is observed that there is a prevalent use of machine translation (MT) among translation learners. The proper use versus abuse of MT can be a critical issue regarding its role in and impact on translation teaching. This exploratory study aims at investigating learners’ and instructors’ knowledge of MT, experience in MT use, perceived MT quality, ethics of MT use, and the perceived relationship between MT and translation training, in order to figure out the usefulness of MT in translation competence acquisition and the necessity of MT training. To this end, we conducted surveys and semi-structured interviews and found that the influence of MT in translation competence acquisition is determined by the properties of MT and learners’ quality. MT is particularly helpful in gaining lexical knowledge and knowledge to ensure translation efficiency, but not in bicultural knowledge. However, such usefulness builds on learners’ language proficiency, analytic ability, and learning motivation. In light of the findings, issues including the sustainability of MT from ethical and linguistic perspectives, and the potential and proper use of MT to inform translator training, are discussed.

Suggested Citation

  • Kanglong Liu & Ho Ling Kwok & Jianwen Liu & Andrew K.F. Cheung, 2022. "Sustainability and Influence of Machine Translation: Perceptions and Attitudes of Translation Instructors and Learners in Hong Kong," Sustainability, MDPI, vol. 14(11), pages 1-29, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6399-:d:822718
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    References listed on IDEAS

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    1. Ramunė Kasperė & Jolita Horbačauskienė & Jurgita Motiejūnienė & Vilmantė Liubinienė & Irena Patašienė & Martynas Patašius, 2021. "Towards Sustainable Use of Machine Translation: Usability and Perceived Quality from the End-User Perspective," Sustainability, MDPI, vol. 13(23), pages 1-17, December.
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    Cited by:

    1. Linling Fu & Lei Liu, 2024. "What are the differences? A comparative study of generative artificial intelligence translation and human translation of scientific texts," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    2. Carlos Cacciuttolo & Yaneth Vásquez & Deyvis Cano & Fernando Valenzuela, 2023. "Research Thesis for Undergraduate Engineering Programs in the Digitalization Era: Learning Strategies and Responsible Research Conduct Road to a University Education 4.0 Paradigm," Sustainability, MDPI, vol. 15(14), pages 1-27, July.

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