Comparative assessment of Bing Translator and Youdao Machine Translation Systems in English-to-Chinese literary text translation
Abstract
This study explores the performance of machine translation of literary texts from English to Chinese. The study compares two machine translation systems, Bing Translator and Youdao Machine Translation, using selected texts from the novel “Nineteen eighty-four” by George Orwell. The data collection includes the original source texts, their machine-generated translations by Bing Translator and Youdao Machine Translation, and comparisons with human reference translations to assess the performance of these systems. The research’s focal point is to evaluate the accuracy, fluency, and appropriateness of translations generated by these two machine translation systems, while also analyzing the post-editing effort required to enhance the quality of the final machine-translated product. The study revealed that despite the presence of flaws in both machine translation systems, Youdao Machine Translation demonstrated superior performance, especially in accurately translating technical terms and idiomatic expressions, making it the more effective option overall. Nevertheless, the translations from Youdao Machine Translation required more substantial post-editing efforts to improve fluency and readability. Conversely, Bing Translator yielded more fluent and natural-sounding translations, albeit with a need for improved accuracy in translating technical terms and idiomatic expressions. The study concludes that while machine translation systems are capable of generating reasonable translations for literary texts, human post-editing remains essential to ensure the final output’s accuracy, fluency, and appropriateness. The study underscores the importance of selecting the appropriate machine translation system based on the nature of the text being translated. It also highlights the critical role of post-editing in refining the quality of machine-translated outputs, suggesting that while machine translation can provide a solid foundation, human intervention is indispensable for achieving optimal accuracy, fluency, and overall readability in literary translations.
References
Ali, M. A. (2020). Quality and Machine Translation: An Evaluation of Online Machine Translation of English into Arabic Texts. Open Journal of Modern Linguistics, 10(5), 524–548. https://doi.org/10.4236/ojml.2020.105030
Araújo, S., & Aguiar, M. (2023). Comparing ChatGPT’s and Human Evaluation of Scientific Texts’ Translations from English to Portuguese Using Popular Automated Translators Notebook for the SimpleText Lab at CLEF 2023.
Bergen, D. (2004). Translation technology and translator training. In: Tommola J (editor). Kieli, teksti ja kääntäminen = Language, Text and Translation. University of Turku. Painosalama Oy. pp. 141–155.
Blanchon, H., & Boitet, C. (2007). For the external evaluation of MT systems using task-based methods (French). Trait. Autom. des Langues, 48(1), 33-65.
Breyel, C., & Grass, T. (2020). Machine translation and “biotranslation”: an unlikely marriage. SKASE Journal of Theoretical Linguistics, 17(4).
Brooks, R. (2020). A Brief History of Machine Translation Technlogy. K International.
Church, K. W., & Hovy, E. H. (1993). Good applications for crummy machine translation. Machine Translation, 8(4), 239–258. https://doi.org/10.1007/BF00981759
Dong, L. (1985). Nineteen Eighty-Four. Hua Cheng Publishing House.
Dun, X., & Liu, S. (2022). Corpus-Based Comparative Study of Google and Youdao Machine Translation Quality. https://doi.org/10.25236/etmhs.2022.040
Ebrahimi, M. A. (2020). Cultural value of translation of proverbs and synopsis. Journal of Research and Knowledge Spreading, 1(1), e11484. https://doi.org/10.20952/jrks1111484
Fitria, T. N. (2021). A review of machine translation tools: The translation’s ability. Language Circle: Journal of Language and Literature, 16(1), 162-176. https://doi.org/10.15294/lc.v16i1.30961
Guerberof-Arenas, A., & Toral, A. (2020). The impact of post-editing and machine translation on creativity and reading experience. Translation Spaces, 9(2), 255–282. https://doi.org/10.1075/ts.20035.gue
Huang, M., & Xue, Q. (2021). Investigating the effects of post-Editing on the productivity of professional translators. International Journal of Emerging Technologies in Learning, 16(1), 135-146.
Huang, M., Lin, Y., Chen, C., & Liu, Y. (2019). Evaluating the effectiveness of post-editing machine translation output: An empirical study of Chinese–English machine translation. International Journal of Computational Linguistics Research, 10(2), 99-107.
Jia, Y., Carl, M., & Wang, X. (2019). Post-editing neural machine translation versus phrase-based machine translation for English–Chinese. Machine Translation, 33(1–2), 9–29. https://doi.org/10.1007/s10590-019-09229-6
Jia, Y., & Lai, S. (2022). Post-Editing Metaphorical Expressions: Productivity, Quality, and Strategies (Journal of Foreign Languages and Cultures). 6, 28–43. https://doi.org/10.53397/hunnu.jflc.202202004
Jibreel, I. (2023). Online Machine Translation Efficiency in Translating Fixed Expressions Between English and Arabic (Proverbs as a Case-in-Point). Theory and Practice in Language Studies, 13(5), 1148-1158. https://doi.org/10.17507/tpls.1305.07
Jiqizhixin. (2018). Defeating two renowned international translation engines. Interpreting NetEase Youdao’s neural machine translation model. Available online: https://www.jiqizhixin.com/articles/2018-12-25-22 (accessed on 23 March 2024).
Koehn, P. (2009). Statistical machine translation. Cambridge University Press.
Lan, M., & Zhao, L. (2021). Contrasting and Analyzing Machine and Human Translation: A Case Study on Red Sorghum. In: 2021 4th International Conference on Humanities Education and Social Sciences (ICHESS 2021). Atlantis Press. pp. 2522-2528. https://doi.org/10.2991/assehr.k.211220.437
Li, H., & Chen, H. (2019). Human vs. AI: An Assessment of the Translation Quality Between Translators and Machine Translation. International Journal of Translation, Interpretation, and Applied Linguistics (IJTIAL), 1(1), 1–12. https://doi.org/10.4018/IJTIAL.2019010104
Microsoft. (2009). Bing Translator. Available online: https://www.bing.com/translator/ (accessed on 9 January 2024).
Mohsen, M. A., Althebi, S., & Albahooth, M. (2023). A scientometric study of three decades of machine translation research: Trending issues, hotspot research, and co-citation analysis. Cogent Arts & Humanities, 10(1), 2242620. https://doi.org/10.1080/23311983.2023.2242620
Mondal, S. K., Zhang, H., Kabir, H. M. D., et al. (2023). Machine translation and its evaluation: a study. Artificial Intelligence Review, 56, 10137–10226. https://doi.org/10.1007/s10462-023-10423-5
Moorkens, J., & O’Brien, S. (2017). Assessing user interface needs of post-editors of machine translation. In: Kenny, D. (editor). Human issues in translation technology. Routledge. pp. 109-130. https://doi.org/10.4324/9781315648934
NetEase. (2007). Youdao Machine Translation. Available online: https://www.youdao.com (accessed on 9 January 2024).
Orwell, G. (1949). The Literature Network, Jalic LLC. Available online: https://www.online-literature.com/orwell/1984/ (accessed on 9 January 2024).
Pondělíčková, A. (2022). Students Versus Machines: A Comparison of Student and Machine Translations.
Rexroth, K. (2024). Encyclopedia Britannica. Available online: https://www.britannica.com/art/literature (accessed on 9 January 2024).
Rivera-Trigueros, I. (2022). Machine translation systems and quality assessment: A systematic review. Language Resources and Evaluation, 56(2), 593–619. https://doi.org/10.1007/s10579-021-09537-5
Rothwell, A., Moorkens, J., Fernández-Parra, et al. (2023). Translation Tools and Technologies. Taylor & Francis.
Škobo, M., & Petričević, V. (2023). Navigating the Challenges and Opportunities of Literary Translation in the Age of AI: Striking a Balance Between Human Expertise and Machine Power. Društvene i Humanističke Studije (Online), 8(2(23)), 317–336. https://doi.org/10.51558/2490-3647.2023.8.2.317
Tang, B., & Chen, S. (2020). A Comparative Analysis of Translations of Medical Texts Produced by Online Machine Translation Software. Chinese Science ＆ Technology Translators Journal, 33(3), 23-26,49. https://doi.org/10.16024/j.cnki.issn1002-0489.2020.03.007
White, J. S., O’Connell, T. A., & O’Mara, F. E. (1994). The ARPA MT Evaluation Methodologies: Evolution, Lessons, and Future Approaches. Proceedings of the First Conference of the Association for Machine Translation in the Americas. AMTA 1994, Columbia, Maryland, USA.
Yang, Y., Liu, R., Qian, X., & Ni, J. (2023). Performance and perception: Machine translation post-editing in Chinese-English news translation by novice translators. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-02285-7
Yang, Y., & Wang, X. (2023). Predicting student translators’ performance in machine translation post-editing: Interplay of self-regulation, critical thinking, and motivation. Interactive Learning Environments, 31(1), 340–354. https://doi.org/10.1080/10494820.2020.1786407
Zhang, F. (2020). On Machine Translation Technology in Legal Translation. Technology Enhanced Foreign Language Education., 1, 53–58.
Zhong, S., & Liu, X. (2023). Cultural specificities of online dictionaries for English learners: Evidence from a user survey and a multimodal discourse analysis. Digital Scholarship in the Humanities, 38(4), 1740-1763. https://doi.org/10.1093/llc/fqad055
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