تحليل الخطأ في ترجمة جوجل: دراسة حالة لنص طبي

نویسندگان

  • عباس أبو التمن فرح قسم الترجمة / كلية الآداب – الجامعة المستنصرية

DOI:

https://doi.org/10.31185/eduj.Vol2.Iss43.2235

چکیده

       أدى توفير الوقت والجهود إلى ضرورة استخدام أنظمة الترجمة الآلية مثل ترجمة جوجل Google Translate  لترجمة النصوص في جميع أنحاء العالم. كانت ترجمة جوجل خلال العقود الماضية محور الإهتمام لكونها أكثر أنظمة الترجمة الآلية استخدامًا وذلك لسرعتها والنتائج شبه الدقيقة التي أظهرتها. نشأت الحاجة لهذه الدراسة من حقيقة أن ترجمة الإنجليزية والعربية الآلية من خلال تطبيق جوجل يوجد فيها العديد من الأخطاء التي يجب تسليط الضوء عليها لأن كلتا اللغتين تنتمي إلى أنظمة لغوية مختلفة. من أجل تقييم جودة ترجمة جوجل، استخدم هذ البحث تحليل الخطأ بهدف تحديد أنواع الأخطاء المختلفة في ترجمة جوجل. ركز هذا التحليل على دراسة عدم تطابق المكونات النحوية والصرفية و المعجمية و الدلالية التي وجدت في النص المصدر و الهدف. دراسة الحالة لهذا البحث هي نص طبي إنجليزي تم اختياره لتقييم دقة وفائدة ترجمة جوجل في ترجمة العبارات و المصطلحات الطبية الإنجليزية. أظهرت النتائج أن الأخطاء الأكثر تكرارا كانت أخطاءا دلالية و نحوية بنسبة (30٪) لكل خطأ بينما حلت الأخطاء المورفولوجية على المرتبة الثانية بنسبة (25%) أما الأخطاء المعجمية فقد حلت في المرتبة الأخيرة بنسبة (15%).

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مراجع

Aqel, K. A. J. D., & Mohammed, F. (2017). A Longitudinal Analysis Study of Writing Errors Made by EFL. British Journal of Education Vol.5, issue 5, pp. 127-145.

Aziz, W., Sousa, S. C. M., & Specia, L. (2012). PET: A tool for post-editing and assessing machine translation. In The Eighth International Conference on Language Resources and Evaluation, Istanbul, Turkey.

Byrne, J. (2006). Technical translation: Usability strategies for translating technical documentation. Dordrecht: Springer.

Castilho S, Moorkens J, Gaspari F, Sennrich R, Sosoni V, Georgakopoulou P, Lohar

P, Way A, Barone AVM, Gialama M (2017). A Comparative Quality Evaluation of PBSMT and NMT using Professional Translators. In: Proceedings of MT Summit XVI. Vol. 108, issue 1, pp. 116–131.

Corder, S.P. (1974). Error Analysis, In Allen, J. L. P. and Corder, S. P. (1974). Techniques in Applied Linguistics. Oxford: Oxford University Press.

Costa A, Ling W, Lu´ıs T, Correia R, Coheur L (2015). A linguistically motivated taxonomy for Machine Translation error analysis. In Machine Translation Jounal. Vol. 29, issue 2, pp:127–161.

Crystal, D. (2003). A Dictionary of Linguistics and Phonetics. 5th ed. Blackwell Publishing Ltd.

Farr´us M, Costa-Juss`a MR, Mari˜no JB, Fonollosa JAR (2010). Linguistic-based Evaluation Criteria to Identify Statistical Machine Translation Errors. In: Proceedings of the 14th Annual Conference of the European Association for Machine Translation (EAMT 2010), Saint-Raphal, France, pp. 167–173.

Federico M., Negri M., Bentivogli L & Turchi, M. (2014). Assessing the Impact of Translation Errors on Machine Translation Quality with Mixed-effects Models. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), Doha, Qatar, pp. 1643–1653.

Jabak, O. (2019). Assessment of Arabic-English translation produced by Google Translate. International Journal of Linguistics, Literature and Translation (IJLLT), Vol. 2, Issue 4, pp. 238-247.

Karami, O. (2014). The Brief View on Google Translate Machine. Paper presented at the meeting of the 2014 Seminar in Artificial Intelligence on Natural Language, German. Reprieved from: https://www.semanticscholar.org/paper/The-brief-view-on-Google-Translate-Machine-Karami/c6f95d543c0b34c4026b9e6cf64decd94b793823

Kirchhoff K, Capurro D, Turner A (2012). Evaluating user preferences in machine translation using conjoint analysis. In: EAMT 2012: Proceedings of the 16th Annual Conference of the European Association for Machine Translation, Italy, pp. 119–126.

Koponen, M. (2010). Assessing machine translation quality with error analysis. In

MikaEL: Electronic Proceedings of the KäTu Symposium on Translation and Interpreting Studies, pp. 1-12. Retrieved from: https://sktl-fi.directo.fi/@Bin/40701/Koponen_MikaEL2010.pdf

Li, H., Graesser, A. C., & Cai, Z. (2014). Comparison of Google translation with human translation. In 27th International Florida Artificial Intelligence Research Society Conference, pp. 190–195. Association for the Advancement of Artificial Intelligence.

Popovic, M. (2018). Error Classification and Analysis for Machine Translation Quality Assessment. J. Moorkens, et al. (eds.), In Translation Quality Assessment, pp. 129-158. Springer International Publishing.

Richards, J. C. & Schmidt, R. (2002). Dictionary of language teaching and applied linguistics. 3rd, ed. London: Longman.

Rustipa, K. (2011). Contrastive Analysis, Error Analysis, Interlanguage and the Implication to Language Teaching. Stikuban University (Unisbank) Semarang. Ragam Jurnal Pengembangan Humanioura, Vol. 11, pp. 16-22. Retrieved from: https://www.semanticscholar.org/paper/Contrastive-Analysis-%2C-Error-Analysis-%2C-and-the-to-Rustipa/255ebdf90f8353aac8347ec8fcbe7e0f66fa92f2

Strevens, P. (1977). Special Purpose Language learning: a perspective. Language Teaching & Linguistics Abstracts Vol. 10 (3), pp. 145-163.

Stymne, S. (2011). Blast: A tool for error analysis of machine translation output. In Proceedings of ACL, pp. 56-61. Portland, Oregon, USA.

Van der wees, M., Bissaza, A. & Monz, C. (2015). Five Shades of Noise: Analyzing Machine Translation Errors in User-Generated Text. In Proceedings of the Workshop on Noisy User-generated Text, pp. 28–37. Retrieved from https://www.researchgate.net/publication/301449172_Five_Shades_of_Noise_Analyzing_Machine_Translation_Errors_in_User-Generated_Text

Widdowson, H.G. (1974). Literary and Scientific Uses of English. In English Language Teaching Journal. Vol. 3 (28), pp. 282-292.

t is shown that lexical and semantic errors have

most impact on sentence level ranking. Furthermore, highly ranked sentences clearly

exhibit low number of grammatical errors, however the relation between grammat-

ical errors and poorly ranked segments remained unclear. Apart from this, high

inter-annotator agreement between two annotators is reported, which contradicts

the results from former studies. The most probable factor is removing words with

position disagreement from calculation which increased the agreement between the

error types

is shown that lexical and semantic errors have

most impact on sentence level ranking. Furthermore, highly ranked sentences clearly

exhibit low number of grammatical errors, however the relation between grammat-

ical errors and poorly ranked segments remained unclear. Apart from this, high

inter-annotator agreement between two annotators is reported, which contradicts

the results from former studies. The most probable factor is removing words with

position disagreement from calculation which increased the agreement between the

errrgest correlation is observed for lexical errors and missing words.

Additional very interesting finding is that the human perception of quality does not

necessarily depend on frequency of the given error type – a sentence with a low

overall score can easily contain less missing words and/or lexical errors than an-

other sentence with

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چاپ شده

2021-06-25

شماره

نوع مقاله

Articles

نحوه استناد به مقاله

فرح ع. أ. ا. (2021). تحليل الخطأ في ترجمة جوجل: دراسة حالة لنص طبي. Journal of College of Education, 2(43), 737-752. https://doi.org/10.31185/eduj.Vol2.Iss43.2235