Perceptions of Machine Translation and Computer-Aided Translation by Professionals and the General Public: A Survey Study Based on Articles in Professional Journals and in the Media

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

This article examines perceptions of MT and CAT among translation professionals and the general public by surveying 124 articles published in the professional journals of ITI Bulletin and MultiLingual and in the Chinese media between 2017 and 2019. Through framing analysis, the following frames about MT and CAT are identified: progress, quality, threat, limitation, cooperation, economic factors, and ethics. Through qualitative analysis of prominent frames, it is also found that attitudes vary between the professional journals and the media about the role of MT as related to human translators. While ITI Bulletin holds a generally conservative attitude, MultiLingual takes a more positive stance towards the applications of MT, and the Chinese media generally hype MT as a potential threat to HT but promote human-machine cooperation as the way out. This study also shows that the ethical and legal issues involving MT and CAT have not been addressed adequately.
Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalInternational Journal of Translation, Interpretation, and Applied Linguistics
Volume2
Issue number2
DOIs
Publication statusPublished - Jul 2020
Externally publishedYes

Bibliographical note

This research was supported by the Great Britain – China Educational Trust. Part of the content of the article was presented in Binhua Wang’s keynote to “the 2019 International Conference on Translation Education” held in the Chinese University of Hong Kong at Shenzhen on 24-25 Aug 2019. Special thanks go to Prof Chunshen Zhu, Dr Yuanyuan Mu, Dr Yingyi Zhuang and other organisers of the conference.

Funding

Open Access Funding Provided by Shandong University (Weihai), China.

Keywords

  • Computer-Aided Translation
  • Frame/Framing
  • ITI Bulletin
  • Machine Translation
  • MultiLingual
  • News Media
  • Perceptions

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