آمار

تعداد نشریات11
تعداد شماره‌ها228
تعداد مقالات2,317
تعداد مشاهده مقاله3,623,807
تعداد دریافت فایل اصل مقاله2,663,795
میرزائیان, وحیدرضا, مقصودی, مجتبی. (1402). مقایسه کارایی ترجمه ماشینی برای آموزش مهارت های درک مطلب در انگلیسی برای اهداف آکادمیک. فناوری آموزش, 18(1), 1-18. doi: 10.22061/tej.2023.9892.2913
وحیدرضا میرزائیان; مجتبی مقصودی. "مقایسه کارایی ترجمه ماشینی برای آموزش مهارت های درک مطلب در انگلیسی برای اهداف آکادمیک". فناوری آموزش, 18, 1, 1402, 1-18. doi: 10.22061/tej.2023.9892.2913
میرزائیان, وحیدرضا, مقصودی, مجتبی. (1402). 'مقایسه کارایی ترجمه ماشینی برای آموزش مهارت های درک مطلب در انگلیسی برای اهداف آکادمیک', فناوری آموزش, 18(1), pp. 1-18. doi: 10.22061/tej.2023.9892.2913
میرزائیان, وحیدرضا, مقصودی, مجتبی. مقایسه کارایی ترجمه ماشینی برای آموزش مهارت های درک مطلب در انگلیسی برای اهداف آکادمیک. فناوری آموزش, 1402; 18(1): 1-18. doi: 10.22061/tej.2023.9892.2913

مقایسه کارایی ترجمه ماشینی برای آموزش مهارت های درک مطلب در انگلیسی برای اهداف آکادمیک

فناوری آموزش
مقاله 1، دوره 18، شماره 1 - شماره پیاپی 69، دی 1402، صفحه 1-18    اصل مقاله (637.11 K)
نوع مقاله: مقاله پژوهشی-شماره انگلیسی
شناسه دیجیتال (DOI): 10.22061/tej.2023.9892.2913
نویسندگان
وحیدرضا میرزائیان* 1؛ مجتبی مقصودی2
1گروه زبان انگلیسی، دانشکده ادبیات، دانشگاه الزهراء، تهران، ایران
2گروه زبان انگلیسی، دانشگاه فرهنگیان، تهران، ایران
تاریخ دریافت: 10 خرداد 1402،  تاریخ بازنگری: 27 مهر 1402،  تاریخ پذیرش: 13 آذر 1402 
چکیده
پیشینه و اهداف: ترجمه ماشینی  یکی از فنون هوش مصنوعی  است که با استفاده از رایانه، متون را از زبانی به زبان دیگر ترجمه می کند. مطالعات محدودی با هدف استفاده از خروجی ترجمه ماشینی برای اهداف زبان آموزی انجام شده است. یکی از جنبه های استفاده از ترجمه ماشینی در آموزش زبان به استفاده از خروجی ترجمه ماشینی برای درک مطلب در یادگیری زبان اشاره دارد. با توجه به این واقعیت که کیفیت خروجی ترجمه ماشینی موضوعی بحث برانگیز بوده است، پژوهش های بیشتری برای بررسی اینکه آیا خروجی آن واجد شرایط کافی برای استفاده به عنوان کمک در دوره های انگلیسی برای اهداف آکادمیک است، مورد نیاز است. این پژوهش با هدف ارزیابی کارایی خروجی ترجمه ماشینی برای دروس درک مطلب انگلیسی برای اهداف آکادمیک از طریق اثبات قابل درک بودن آن و بررسی ادراک دانشجویان انجام شد. این پژوهش تلاشی برای ارزیابی کیفیت خروجی ترجمه ماشینی از نظر قابل درک بودن خروجی ترجمه ماشینی برای دانشجویان شرکت کننده بود.
روش‌‌: برای همگن‌سازی شرکت‌کنندگان از نظر تسلط به زبان انگلیسی، از آزمون تعیین سطح زبانی آکسفورد استفاده شد. دو نسخه از آزمون درک مطلب یعنی ترجمه انسانی  و ترجمه ماشینی  به 140 دانشجوی ایرانی در مقطع کارشناسی دانشگاه فرهنگیان داده شد. این آزمون شامل 25 آیتم چند گزینه ای بود که درک شرکت کنندگان از اطلاعات مندرج در متن و همچنین درک مرتبه بالاتری که مستلزم استنباط و نتیجه گیری بود را ارزیابی می کرد. به منظور بررسی پایایی آزمون‌ها از فرمول KR-21 استفاده شد و نتایج نشان داد که هر دو آزمون ترجمه ماشینی (83/0) و آزمون ترجمه انسانی (78/0) پایایی دارند. به منظور بررسی ادراک شرکت‌کنندگان از کارایی خروجی ترجمه ماشینی که در آزمون با آن مواجه شدند، مصاحبه های نیمه ساختاریافته با برخی از شرکت کنندگان انجام شد.
یافته‌ها: از طریق آزمون‌های ناپارامتریک مانند آزمون‌های اسپیرمن و من ویتنی مشخص شد به طور کلی، خروجی ترجمه ماشینی با خروجی ترجمه انسانی قابل رقابت است. علاوه بر این، از نظر مهارت‌های فرعی درک مطلب، کیفیت دو ترجمه قابل مقایسه بود. علاوه بر این، یافته‌های حاصل از مصاحبه نشان داد دانش‌آموزان علی‌رغم مشکلات جزئی موجود در خروجی مانند خطاهای صرفی- نحوی یا معادل‌های واژگانی نامناسب، ترجمه ماشینی را کمکی اساسی برای فعالیت‌های درک مطلب خود می دانستند.
نتیجه‌گیری: این پژوهش نشان داد کارایی خروجی ترجمه ماشینی وابسته به خواننده هدف و وابسته به متن است، زیرا هم توسط ویژگی های خواننده و هم متن تعیین می گردد. این موضوع در نمرات کسب شده توسط دانشجویان در گروه های مختلف کاملا مشهود بود. بنا براین پژوهش حاضر درک پژوهشگران را از ترجمه ماشینی و تاثیر آن بر درک مطلب افزایش می دهد. 
کلیدواژه‌ها
ادراک پذیری؛ انگلیسی آکادمیک؛ ترجمه ماشینی؛ درک مطلب
موضوعات
ترجمه ماشینی
عنوان مقاله [English]
Benchmarking Machine Translation Efficacy for Teaching EAP Reading Comprehension Skills
نویسندگان [English]
V. Mirzaeian1؛ M. Maghsoudi2
1Department of English, Faculty of Literature, Alzahra University, Tehran, Iran
2Department of English Language Teaching, Farhangian University, Tehran, Iran
چکیده [English]
Background and Objectives: Although Machine Translation (MT) is extensively researched within the field of Artificial Intelligence (AI) and translation studies, few studies have attempted to implement MT output in foreign language teaching (FLT). One potential aspect of using MT in FLT refers to the implementation of MT output for reading comprehension. Considering the existing gap in the body of research on this issue, the present study aimed to investigate whether MT output is qualified enough to be used as an aid in EAP reading comprehension courses. More specifically, this study intended to benchmark the efficacy of MT output for EAP reading comprehension courses based on the data obtained from testing its comprehensibility and probing the students’ perceptions. To achieve the objectives of the study, MT was operationally defined as quality assessment in terms of output efficacy, a combination of usability and comprehensibility, which mirrors the ultimate goal of MT use in EAP reading comprehension courses, from the users' or target readers’ standpoint. Within this perspective, the current research was an attempt to assess the quality of MT output in terms of comprehensibility and the degree to which MT output might be comprehensible to the EAP students participating in this study.
Materials and Methods: The participants of the study, 140 Iranian undergraduate university students majoring in the field of education at Farhangian University, Iran, were selected based on simple random sampling. Oxford Quick Placement Test was used to homogenize them in terms of English proficiency. Two versions of a reliable reading comprehension test, human translation (HT) and Machine Translation (MT), were given to. This test included 25 multiple-choice items, assessing the participants' literal comprehension of information stated in the passage as well as higher-order comprehension that required making inferences and conclusions. In particular, the items measured textual coherence, inference, reference, scanning, skimming, and word-meaning inference. To test the reliability of the tests, the KR-21 formula was applied and the results showed that both HT test (.83) and MT test (.78) were reliable. To investigate the perceptions of the participants on the efficacy of the MT output they encountered on the test, semi-structured interviews were conducted with some of the participants in Persian. 
Findings: With reference to the results of non-parametric tests such as Spearman’s rho, and Mann-Whitney Tests, and considering the observed effect sizes (Cohen’s d), it was revealed that, generally, the efficacy of MT output is comparable to that of HT. Moreover, in terms of reading comprehension sub-skills, the qualities of the two translations were comparable with regard to scanning, and inference, but not skimming and reference. Furthermore, the findings from the interview indicated that the students perceive MT to be a seminal aid for their EAP reading comprehension activities despite the minor problems that exist in the output such as morpho-syntactic errors or inappropriate lexical equivalents.
Conclusions: The present study confirmed the fact that the efficacy of MT output is target-reader-dependent and text-dependent since it is determined both by the characteristics of the readers, such as their disciplines, and text features, as demonstrated by the significant differences in comprehension levels of the same readers measured by the same questions for HT and MT output. Accordingly, this study shed limelight on comprehensibility as a criterion of MT output efficacy; that is to say, it has to be reminded that MT quality needs to be defined as a context-bound and target-reader-specific concept.
کلیدواژه‌ها [English]
Comprehensibility, EAP, Machine Translation, Reading Comprehension
مراجع
[1] Tudor, I. Using translation in ESP. ELT Journal, 1987;41(4): 268-273. https://doi.org/10.1093/elt/41.4.268

[2] Plastina, A. F. Mental translation in the ESP classroom: a case study on processing reading comprehension. Mental Translation in the ESP Classroom, 2006; 1000-1013.

[3] Groves, M., & Mundt, K. Friend or foe? Google Translate in language for academic purposes. English for Specific Purposes, 2015; 37: 112-121.

[4] Bahri, H., & Mahadi, T. Google Translate as a supplementary tool for learning Malay: A case study at Universiti Sains Malaysia. Advances in Language and Literary Studies, 2016; 7(3): 161–167. http://dx.doi.org/10.5755/j01.sal.0.21.1469

[5] Lee, S. M. The impact of using machine translation on EFL students’ writing. Computer Assisted Language Learning, 2020; 33(3): 157-175.

https://doi.org/10.1080/09588221.2018.1553186

[6] Tsai, S. C. Using google translate in EFL drafts: a preliminary investigation. Computer Assisted Language Learning, 2019; 32(5-6): 510-526. DOI:10.1080/09588221.2018.1527361

[7] Suhono, S., Zuniati, M., Pratiwi, W., & Hasyim, U. A. A. Clarifying Google Translate Problems Of Indonesia-English Translation Of Abstract Scientific Writing. Proceedings of the 2nd Workshop on Multidisciplinary and Applications (WMA). 2020; Padang, Indonesia.

http://dx.doi.org/10.4108/eai.24-1-2018.2292399

[8] Knowles, C. L. Investigating instructor perceptions of online machine translation and second language acquisition within most commonly taught language courses. [dissertation]. The University of Memphis; 2016.

[9] Rushwan, I. M. H. The role of translation in developing ESP learners’ reading comprehension skills-A case study of medical students at Najran University-ksa. International Journal of Applied Linguistics and English Literature, 2017; 6(3): 243-253.

https://doi:10.7575/aiac.ijalel.v.6n.3p.243

[10] Mesbah, M., & Faghani, M. Task-based and grammar translation teaching methods in teaching reading comprehension to nursing students: An action research. Aula Orientalis, 2015; 1: 319-25.

[11] Avand, A. Q. Using translation and reading comprehension of ESP learners. The Asian ESP Journal, 2009; 5(1): 44-60.

[12] Kavaliauskien?, G. Proficiency in reading, writing and translation skills: ESP aspect. Vertimo Studijos, 2009; 2(2): 171-184.

[13] Malekan, F., & Hajimohammadi, R. The Relationship between Iranian ESP Learners' Translation Ability and Resilience in Reading Comprehension. International Journal of Education and Literacy Studies,  2017; 5(2): 47-52.

[14] Pham, C. T. K. Reading comprehension and translation performance of English linguistics students of Hung Vuong University: A correlational study. International Journal of English Language and Translation Studies, 2017; 5: 79-85.

[15] Tavakoli, M., Shafiei, S., & Hatam, A. H. The relationship between translation tests and reading comprehension: a case of Iranian University students. Iranian Journal of Applied Language Studies, 2012; 4(1): 193-211.

https://doi:10.22111/IJALS.2012.1353

[16] Laviosa, S., & Cleverton, V. Learning by translating: A contrastive methodology for ESP learning and translation. Scripta Manent, 2015; 2(1): 3-12.

[17] Leonardi, V. Teaching business English through translation. Journal of Universal Language, 2009; 10(1): 139-153. https://doi:10.22425/jul.2009.10.1.139

[18] Mažeikiene. VTranslation as a method in teaching ESP: An inductive thematic analysis of literature. Journal of Teaching English for Specific and Academic Purposes .2019; 6(3): 513-523.

https://doi.org/10.22190/JTESAP1803513M

[19] Novita, L., & Mustafha, B. Translation as a Pedagogical Device for Improving Students’ Reading Comprehension in the ESP text. In Proceedings of the 1st International Conference on Business, Law And Pedagogy, ICBLP 2019. European Alliance for Innovation (EAI).

http://dx.doi.org/10.4108/eai.13-2-2019.2286060

[20] Levy, M., & Steel, C. Language learner perspectives on the functionality and use of electronic language dictionaries. ReCALL: the Journal of EUROCALL, 2015; 27(2): 177. https://doi:10.1017/S095834401400038X

[21] Lee, S. M., & Briggs, N. Effects of using machine translation to mediate the revision process of Korean university students’ academic writing. ReCALL, 2021; 33(1): 18-33.

https://doi:10.1017/S0958344020000191

[22] Nino, A. Evaluating the use of machine translation post-editing in the foreign language class. Computer Assisted Language Learning, 2008; 21(1): 29–49.

http://doi.org/10.1080/ 09588220701865482

[23] Melby, A. K. The translation profession and the computer. CALICO Journal, 2013; 1(1): 55-57.

https://doi.org/10.1558/cj.v1i1.55-57

[24] Garcia, I., & Pena, M. I. Machine translation-assisted language learning: writing for beginners. Computer Assisted Language Learning, 2011; 24(5): 471-487.

https://doi:10.1080/09588221.2011.582687

[25] Chung, E. S., & Ahn, S. The effect of using machine translation on linguistic features in L2 writing across proficiency levels and text genres. Computer Assisted Language Learning, 2021; 5(3): 1-26. https://doi:10.1080/09588221.2020.1871029

[26] Chang, C. K., & Hsu, C. K. A mobile-assisted synchronously collaborative translation–annotation system for English as a foreign language (EFL) reading comprehension. Computer Assisted Language Learning, 2011; 24(2): 155-180. https://doi:10.1080/09588221.2010.536952

[27] Arnold, D., & Balkan, L. Machine Translation. 2001; USA: Blackwell Publishers.

[28] Alhaisoni, An investigation of Saudi EFL university students’ attitudes towards the use of google translate. International Journal of English Language Education, 2017; 5(1): 72-82. https://doi:10.5296/ijele.v5i1.10696

[29] Siregar, R. Exploring the undergraduate student's perception on translation - A preliminary step to teach translation in EFL classes. English Language Teaching, 2018; 11(9): 90-101. DOI:10.5539/elt.v11n9p90

[30] Shterionov, D., Superbo, R., Nagle, P., Casanellas, L., O’dowd, T., & Way, A. Human versus automatic quality evaluation of NMT and PBSMT. Machine Translation, 2018; 32(3): 217-235.

[31] Taleghani, M., Pazouki, E., & Ghahraman, V. The Correlation of Machine Translation Evaluation Metrics with Human Judgement on Persian Language. Journal of Language and Translation, 2019; 9(3): 43-55.

https://doi: 20.1001.1.20088590.2019.9.3.4.6

[32] Bahdanau, D., Cho, K., & Bengio, Y. Neural machine translation by jointly learning to align and translate. 2014; Retrieved online on February 23, 2020 from arXiv preprint arXiv:1409.0473.

[33] Napitupulu, S. Analyzing Indonesian-English abstracts translation in view of translation errors by google translate. International Journal of English Language and Linguistics Research, 2017; 5(2): 15-23.

[34] Salah Habeeba, & Muhammed Investigating the effectiveness of google translate among Iraqi students. International Journal of Innovation, Creativity and Change, 2019; 7(8): 112-122.

[35] Cetiner, C., &Isisag, K. Undergraduate level translation students’ attitudes towards machine translation post-editing training. International Journal of Languages’ Education and Teaching, 2019; 7(1): 110-120. https://doi:10.18298/ijlet.3242

[36] Koehn, P., & Knowles, R. Six challenges for neural machine translation. 2017; Retrieved online on February 23, 2020 from arXiv preprint arXiv:1706.03872.

[37] Chang, C., & Yamada, M. Translation tasks for learning collocations: Effects of machine translation plus post-editing and sight translation. English Teaching and Learning, 2020; 3(5): 87–101. http://doi.org/ 10.1007/s42321-020-00059-x

[38] Toral, A., & Sánchez-Cartagena, V. M. A multifaceted evaluation of neural versus phrase-based machine translation for 9 language directions. 2017; Retrieved online on February 23, 2020 from arXiv preprint arXiv:1701.02901.

[39] Jimenez, C. The role of translation technologies in Spanish language learning. Journal of Spanish Language Teaching, 2018; 4(4): 141-160.

http://doi.org/10.1080/23247797.2017.1408949

[40]  Abdelaal, N., & Alazzawie, A. Machine Translation: The case of Arabic-English translation of news texts. Theory and Practice in Language Studies, 2020; 10(4): 408–418. http://doi.org/10.17507/tpls.1004.09

[41] Roturier, J. An investigation into the impact of controlled English rules on the comprehensibility, usefulness and acceptability of machine-translated technical documentation for French and German users. [dissertation]. Dublin City University; 2006.

[42] Alderson, C. Reading Assessment. 2000; New York: Cambridge University Press.

[43] Lewkowicz, J. A. Authenticity in language testing: some outstanding questions. Language testing, 2000; 17(1): 43-64.

[44] Bachman, L. F., & Palmer, A. S. Language Testing in Practice: Designing and Developing Useful Language Tests. 1996; Oxford: Oxford University Press.

[45] Bentivogli, L., Bisazza, A., Cettolo, M., & Federico, M. Neural versus phrase-based machine translation quality: a case study. 2016; Retrieved online on February 23, 2020 from arXiv preprint arXiv:1608.04631.

[46] Doherty, S. Investigating the effects of controlled language on the reading and comprehension of machine translated texts: A mixed-methods approach [dissertation]. Dublin City University; 2012.

[47] Anuyahong, B. A study of English reading strategies and reading motivation used by Thai-Nichi institute of technology students. Automotive Engineering, 2011; 1(14): 21-30.

[48] Becirovic, S., Brdarevic-Celjo, A., & Sinanovic, J. The Use of Metacognitive Reading Strategies among Students at International Burch University: A Case Study. European Journal of Contemporary Education, 2017; 6(4): 645-655. https://doi:10.13187/ejced.2017.4.645

[49] Ganji, M., Yarahmadzehi, N., & Sasani, N. English Major Students’ Awareness of Metacognitive Reading Strategies: Gender and Academic Level in Focus. Issues in Language Teaching, 2018; 7(2): 91-119. https://doi.org/10.22054/ilt.2019.46700.428

[50] Karnal, A. R., & Pereira, V. V. Reading strategies in a L2: A study on machine translation. Reading Matrix: An International Online Journal, 2015; 15(2): 69-80.

[51] Brantmeier, C. Effects of reader's knowledge, text type, and test type on L1 and L2 reading comprehension in Spanish. The Modern Language Journal, 2005; 89(1), 37-53. https://doi.org/10.1111/j.0026-7902.2005.00264.x

[52] Chen, C. M., & Lin, Y. J. Effects of different text display types on reading comprehension, sustained attention and cognitive load in mobile reading contexts. Interactive Learning Environments, 2016; 24(3): 553-571.

https://doi:10.1080/10494820.2014.891526

[53] Coelho, C. L. G., & Correa, J. Reading comprehension: Cognitive abilities and types of text. Psico, 2017; 48(1): 40-49.

[54] Matheson, I. Unpacking Reading Comprehension by Text Type: An Examination of Reading Strategy Use and Cognitive Functioning in Poor and Typically-Achieving Comprehenders [dissertation]. Queen’s University, Kingston; 2018.

[55] Uysal, P. K., & Bilge, H. An investigation on the relationship between reading fluency and level of reading comprehension according to the type of texts. International Electronic Journal of Elementary Education, 2019; 11(2): 161-172.

https://doi:10.26822/iejee.2019248590

[56] Castilho, S., & Guerberof Arenas, A. Reading comprehension of machine translation output: what makes for a better read? In P´erez-Ortiz, S´anchez-Mart´?nez, Espl`a-Gomis, Popovi´c, Rico, Martins, Van den Bogaert, Forcada (eds.). 2018; Proceedings of the 21st Annual Conference of the European Association for Machine Translation  79–88.

[57] Scarton, C., & Specia, L. A reading comprehension corpus for machine translation evaluation. 2016; In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), 3652-3658.

[58] O’Brien, S., & Ehrensberger-Dow, M. MT Literacy—A cognitive view. Translation, Cognition & Behavior, 2020; 3(2): 145-164. https://doi.org/10.1075/tcb.00038.obr

[59] Bowker, L. Machine translation literacy instruction for international business students and business English instructors. Journal of Business & Finance Librarianship, 2020; 25(1-2): 25-43. DOI:10.1080/08963568.2020.1794739

[60] Chen, S. (2019). Effects of machine translation on foreign language learning and teaching. Journal of Language Teaching and Research, 10(5): 991-1000.

https://doi.org/10.17507/jltr.1005.13

[61] Li, D., & Liu, J. (2020). The impact of machine translation on foreign language learning. Open Journal of Linguistics, 10(2): 255-262. https://doi.org/10.4236/ojml.2020.102016

[62] Liu, J., & Li, D. (2020). The impact of machine translation on foreign language teaching. International Journal of Emerging Technologies in Learning, 15(11): 143-159.

https://doi.org/10.3991/ijet.v15i11.14627

آمار
تعداد مشاهده مقاله: 1,111
تعداد دریافت فایل اصل مقاله: 660


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب