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An Analysis of Semantic Errors in Machine-Translated English Compositions by Korean EFL College Students

  • 투고 : 2022.07.26
  • 심사 : 2022.09.04
  • 발행 : 2022.09.30

초록

The purpose of this research is to investigate the types of semantic errors made by MT in translating EFL college students' original drafts written in Korean into English. Specifically, this study attempts to find out 1) what types of semantic errors are most frequently committed by MT? and 2) how students feel about the quality of the MT-produced output? The findings from this study indicated that MT produced the errors related to accuracy (47%) the most, followed by the errors related to fluency and ambiguity (14.6% respectively). Students were well aware of the errors with accuracy and fluency but had limited ability to check the errors with ambiguity. Based on the findings, this study suggests pedagogical implications which can be implemented in L2 writing classrooms.

키워드

참고문헌

  1. I. Garcia, "Can Machine Translation Help the Language Learner?," in Proc. International Conference ICT for Language Learning, pp. 1-4, 2016
  2. J. R. Jolley and L. Maimone, "Free Online Machine Translation: Use and Perceptions by Spanish Students and Instructors," in A. Moeller (Ed.), Learn Language, Explore Cultures, Transform Lives (pp. 181-200). Minneapolis: 2015 Central States Conference on the Teaching of Foreign Languages.
  3. S. M. Lee, "Korean College Students' Perceptions toward the Effectiveness of Machine Translation on L2 Revision," Multimedia- Assisted Language Learning, Vol. 22, No. 4, pp. 206-225, 2019. https://doi.org/10.15702/MALL.2019.22.4.206
  4. S. M. Lee, "The Impact of Using Machine Translation on EFL Students' Writing," Computer Assisted Language Learning, Vol. 33, No. 3, pp. 157-175, 2020. DOI: 10.1080/09588221.2018.1553186.
  5. E. O'Neill, "Measuring the Impact of Online Translation on FL Writing Scores," The IALLT Journal, Vol. 46, No. 2, pp. 1-39, 2016. https://doi.org/10.17161/iallt.v46i2.8560
  6. K. D. White and E. Heidrich, "Our Policies, their Text: German Language Students' Strategies with Beliefs about Web-based Machine Translation," Die Unterrichtspraxis/Teaching German, Vol. 46, No. 2, pp. 230-250, 2013. https://doi.org/10.1111/tger.10143
  7. J. Y. Baek and K. H. Rha, "Korean College Students' Self-Directed Use of Machine Translator in an English Writing Class During the Digital Era: Is This a Crisis or a Chance?," Journal of Safety and Crisis Management, Vol. 11, No. 6, pp. 27-34, 2021. DOI: 10.14251/jscm.2021.6.27.
  8. S. Doherty and D. Kenny, "The Design and Evaluation of a Statistical Machine Translation Syllabus for Translation Students," The Interpreter and Translator Trainer, Vol. 8, No. 2, pp. 295-315, 2014. DOI: 10.1080/1750399X.2014.937571.
  9. A. Nino, "Exploring the Use of Online Machine Translation for Independent Language Learning," Research in Learning Technology, Vol. 28, pp. 1-32, 2020. DOI: 10.25304/rlt.v28.2402.
  10. C. S. Lee, "Stylometric Comparative Analysis of Style in Human vs. Machine Literary Translations," The Journal of Translation Studies, Vol. 20, No. 2, pp. 111-130, 2019. DOI: 10.15749/jts.2019.20.2.005.
  11. M. Y. Ahn, "MT Problems and its MTPE Suggestion with Regard to Structural Differences between English and Korean," The Mirae Journal of English Language and Literature, Vol. 25, No. 1, pp. 103-130, 2020. DOI: 10.46449/MJELL.2020.02.25.1.103.
  12. Y. Hwang, Y. Kim, and K. Jung, "Context-Aware Neural Machine Translation for Korean Honorific Expressions," Electronics, Vol. 10, No. 13, 1589, 2021. DOI: 10.3390/electronics10131589.
  13. K. Alasta and L. Sujarwati, "Lexical Errors Produced by Google Translate in Translating "Putri Serindang Bulan" to English Language," Journal of Development and Innovation in Language and Literature Education, Vol. 2, No. 2, pp. 200-211, 2021. DOI: 10.52690/jadila.v2i2.196.
  14. M. Yamada, "A Study of the Translation Process through Translators' Interim Products," Interpreting and Translation Studies, No. 9, pp. 159-176, 2009.
  15. J. Vardaro, M. Schaeffer, and S. Hansen-Schirra, "Translation Quality and Error Recognition in Professional Neural Machine Translation Post-Editing," Informatics, Vol. 6, No. 3, pp. 1-29, 2019. DOI: 10.3390/informatics6030041.
  16. N. F. M. Sholikhah, and R. N. Indah, "Common Lexical Errors Made by Machine Translation on Cultural Text," Jurnal Linguistiks Terapan dan Pendidikan Bahasa Inggris, Vol. 8, No. 1, pp. 39-51, 2021. https://doi.org/10.34001/edulingua.v8i1.1524
  17. M. P. A. Llach, "A Critical Review of the Terminology and Taxonomies Used in The Literature on Lexical Errors," A Journal of English and American Studies, No. 31, pp. 11-24, 2005.
  18. S. H. Han, "Factor Analysis of Machine Translation Quality," Interpreting and Translation Studies, Vol. 25, No. 2, pp. 147-170, 2021. DOI: 10.22844/its.2021.25.2.147.