Neural Machine Translation in Second Language Writing: a Systematic Review

d3070764-2cf6-49e2-8003-e31cc6433b1c20250213065125914naun:naunmdt@crossref.orgMDT DepositInternational Journal of Education and Information Technologies2074-131610.46300/9109http://www.naun.org/cms.action?id=3037312202431220241810.46300/9109.2024.18https://npublications.com/journals/educationinformation/2024.phpNeural Machine Translation in Second Language Writing: a Systematic ReviewYuchengMaoJiangxi Normal University, No.99, Ziyang Avenue, Nanchang, 330022 ChinaFulanLiuJiangxi Normal University, No.99, Ziyang Avenue, Nanchang, 330022 ChinaThe advent of artificial intelligence has led to the emergence of a novel form of machine translation (MT), namely neural machine translation (NMT), which has significant potential for application in the field of second language (L2) writing. This review aims to synthesize the various approaches to effectively integrating NMT into L2 writing pedagogy and practice. A comprehensive search was conducted in a well-established database based on specific criteria. The findings indicate that NMT is an effective tool for developing L2 writing skills and that it is particularly well-suited to advanced L2 writers, as higher proficiency levels result in more critical reflection on NMT output. Finally, this review demonstrates that NMT has significant pedagogical implications. It serves as an invaluable online reference tool for L2 writing, provided that teachers introduce students to its benefits and limitations by implementing various teaching approaches.123120241231202414315314https://npublications.com/journals/educationinformation/2024/a282008-014(2024).pdf10.46300/9109.2024.18.14https://npublications.com/journals/educationinformation/2024/a282008-014(2024).pdf10.1109/2.511968Paris, C., & VanderLinden, K. (1996). An interactive support tool for writing multilingual manuals. Computer, 29(7), 49-56. https://doi.org/10.1109/2.511968. 10.1016/j.heliyon.2023.e18595Polakova, P., & Klimova, B. (2023). Using DeepL translator in learning English as an applied foreign Language - An empirical pilot study. Heliyon, 9(8), Article e18595. https://doi.org/10.1016/j.heliyon.2023.e18595. 10.1080/09588221.2011.582687Garcia, I., & Pena, M. (2011). Machine translation-assisted language learning: Writing for beginners. Computer Assisted Language Learning, 24(5), 471-487. https://doi.org/10.1080/09588221.2011.582687. Treece, R. (2011). “Yo Lata Mosca Espalda”: Student compositions and online translators. In L. Gómez Chove et al. (Eds.), INTED2011 Proceedings (pp. 6410-6414). Valencia: IATED. https://library.iated.org/view/TREECE2011YO (Accessed: 02-05-24). Jolley, J. R., & Maimone, L. (2015). Free online machine translation: Use and perceptions by Spanish students and instructors. In J. Aleidine (Ed.), Learn Language, Explore Cultures, Transform. Lives (pp. 181-200). Lincoln: University of Nabraska-Lincoln. https://digitalcommons.unl.edu/cgi/viewcontent.cgi?articl e=1298&context=teachlearnfacpub#page=202 (Accessed: 02-05-24). 10.1007/s40979-016-0013-yRogerson, A. M., & McCarthy, G. (2017). Using Internet based paraphrasing tools: Original work, patchwriting or facilitated plagiarism? International Journal for Educational Integrity, 13(2), 1-15. https://doi.org/10.1007/s40979-016-0013-y. 10.1145/1835449.1835687Barrón-Cedeño, A. (2010). On the mono- and cross-language detection of text reuse and plagiarism. In F. Crestani et al. (Eds.), Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010 (pp. 914-915). New York: Association for Computing Machinery. https://riunet.upv.es/bitstream/handle/10251/16012/tesis UPV3833.pdf?sequence=1. (Accessed: 02-05-24). 10.1080/09588221.2021.1901745Lee, S.-M. (2023). The effectiveness of machine translation in foreign language education: A systematic review and meta-analysis. Computer Assisted Language Learning, 36(1-2), 103-125. https://doi.org/10.1080/09588221.2021.1901745. Fredholm, K. (2015). Online translation use in Spanish as a foreign language essay writing: Effects on fluency, complexity and accuracy. Revista Nebrija de Lingüística Aplicada a la Enseñanza de Lenguas, 9(18), 7-24. https://doi.org/10.26378/rnlael918248. 10.1007/978-3-319-91241-7_11O’Brien, S., Simard, M., & Goulet, M. (2018). Machine translation and self-post-editing for academic writing support: Quality explorations. In J. Mookens et al. (Eds.), Translation quality assessment: From principles to practice (pp. 237-262). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-91241-7_11. 10.17161/iallt.v46i2.8560O’Neill, E. (2016). Measuring the impact of online translation on FL writing scores. The IALLT Journal, 46(2), 1-39. https://doi.org/10.17161/iallt.v46i2.8560. 10.1080/09588221.2018.1553186Lee, S.-M. (2020). The impact of using machine translation on EFL students’ writing. Computer Assisted Language Learning, 33(3), 157-175. https://doi.org/10.1080/09588221.2018.1553186. 10.1016/j.esp.2019.07.001Stapleton, P., & Kin, B. (2019). Assessing the accuracy and teachers’ impressions of Google Translate: A study of primary L2 writers in Hong Kong. English for Specific Purposes, 56, 18-34. https://doi.org/10.1016/j.esp.2019.07.001. 10.1080/09588221.2018.1527361Tsai, S.-C. (2019). Using Google Translate in EFL drafts: A preliminary investigation. Computer Assisted Language Learning, 32(5-6), 510-526. https://doi.org/10.1080/09588221.2018.1527361. 10.5209/clac.63295Fredholm, K. (2019). Effects of Google Translate on lexical diversity: Vocabulary development among learners of Spanish as a foreign language. Revista Nebrija de Lingüística Aplicada a la Enseñanza de Las Lenguas, 13(26), 98-117. https://doi.org/10.26378/rnlael1326300. 10.4995/eurocall.2018.10140Kol, S., Schcolnik, M., & Spector-Cohen, E. (2018). Google Translate in academic writing courses? The EuroCALL Review, 26(2), 50-57. https://doi.org/10.4995/eurocall.2018.10140. 10.29140/jaltcall.v14n1.j221Briggs, N. (2018). Neural machine translation tools in the language learning classroom: Students’ use, perceptions, and analyses. The JALT CALL Journal, 14(1), 3-24. https://doi.org/10.29140/jaltcall.v14n1.j221. 10.1017/s0958344020000191Lee, S.-M., & Briggs, N. (2021). Effects of using machine translation to mediate the revision process of Korean university students’ academic writing. ReCALL, 33(1), 18-33. https://doi.org/10.1017/S0958344020000191. 10.25304/rlt.v28.2402Niño, A. (2020). Exploring the use of online machine translation for independent language learning. Research in Learning Technology, 28, Article 2402. https://doi.org/10.25304/rlt.v28.2402. 10.1080/09588221.2020.1871029Chung, E. S., & Ahn, S. (2022). The effect of using machine translation on linguistic features in L2 writing across proficiency levels and text genres. Computer Assisted Language Learning, 35(9), 2239-2264. https://doi.org/10.1080/09588221.2020.1871029. Xu, J. (2020). Machine translation for editing compositions in a Chinese language class: Task design and student beliefs. Journal of Technology and Chinese Language Teaching, 11(1), 1-18. http://www.tclt.us/journal/2020v11n1/xu.pdf (Accessed: 02-05-24). 10.1080/09588221.2020.1799412Tsai, S.-C. (2022). Chinese students’ perceptions of using Google Translate as a translingual CALL tool in EFL writing. Computer Assisted Language Learning, 35(5-6), 1250-1272. https://doi.org/10.1080/09588221.2020.1799412. Wu, Y., Schuster, M., Chen, Z., Le, Q. V., & Norouzi, M. (2016). Google’s neural machine translation system: Bridging the gap between human and machine translation. https://doi.org/10.48550/arXiv.1609.08144. 10.1016/j.linged.2019.100750Jones, R. H. (2021). The text is reading you: Teaching language in the age of the algorithm. Linguistics and Education, 62, Article 100750. https://doi.org/10.1016/j.linged.2019.100750. 10.46300/9106.2023.17.21Sidorenko, A., Klenov, N., Soloviev, I., Bakurskiy, S., Boian, V., Morari, R., Savva, Y., Lomakin, A., Sidorenko, L., Sidorenko, S., Sidorenko, I., Severyukhina, O., Fedotov, A., Salamatina, A., & Vakhrushev, A. (2023). Base elements for artificial neural network: Structure modeling, production, properties. International Journal of Circuits, Systems and Signal Processing, 17, 177-183. https://doi.org/10.46300/9106.2023.17.21. 10.1017/s0958344009000172Niño, A. (2009). Machine translation in foreign language learning: Language learners’ and tutors’ perceptions of its advantages and disadvantages. ReCALL, 21(2), 241-258. https://doi.org/10.1017/S0958344009000172. 10.14705/rpnet.2021.54.1313Delorme Benites, A., Cotelli Kureth, S., Lehr, C., & Steele, E. (2021). Machine translation literacy: A panorama of practices at Swiss universities and implications for language teaching. In N. Zoghlami et al. (Eds), CALL and professionalisation: short papers from EUROCALL 2021 (pp. 80-87). https://doi.org/10.14705/rpnet.2021.54.1313. 10.1007/s10639-022-11194-2Klimova, B., Pikhart, M., Benites, A., Lehr, C., & Sanchez-Stockhammer, C. (2023). Neural machine translation in foreign language teaching and learning: A systematic review. Education and Information Technologies, 28(1), 663-682. https://doi.org/10.1007/s10639-022-11194-2. 10.1080/23247797.2017.1408949Jiménez-Crespo, M. A. (2018). The role of translation technologies in Spanish language learning. Journal of Spanish Language Teaching, 4(1), 1-13. https://doi.org/10.1080/23247797.2017.1408949. 10.46300/9106.2023.17.9Weerasekara, S., Premarathne, S., & Jayaratne, K. L. (2023). A machine learning approach to enhance real-time harbor management. International Journal of Circuits, Systems and Signal Processing, 17, 76-82. https://doi.org/10.46300/9106.2023.17.9. 10.46300/9106.2024.18.1Vasyl, M., Sadiq, A., Andriy, S., Georgi, D., & Tomasz, G. (2024). Stochastic machine learning models for mutation rate analysis of malignant cancer cells in patients with acute lymphoblastic leukemia. International Journal of Circuits, Systems and Signal Processing, 18, 1-12. https://doi.org/10.46300/9106.2024.18.1. 10.1016/j.system.2021.102464Cancino, M., & Panes, J. (2021). The impact of Google Translate on L2 writing quality measures: Evidence from Chilean EFL high school learners. System, 98, Article 102464. https://doi.org/10.1016/j.system.2021.102464. Lee, S.-M. (2022). Different effects of machine translation on L2 revisions across students’ L2 writing proficiency levels. Language Learning & Technology, 26(1), 1-21. https://hdl.handle.net/10125/73490 (Accessed: 02-05-24). 10.1080/09588221.2023.2207603Lo, S. (2023). Neural machine translation in EFL classrooms: Learners’ vocabulary improvement, immediate vocabulary retention and delayed vocabulary retention. Computer Assisted Language Learning. https://doi.org/10.1080/09588221.2023.2207603. 10.1016/j.system.2020.102408Chon, Y. V., Shin, D., & Kim, G. E. (2021). Comparing L2 learners’ writing against parallel machine-translated texts: Raters’ assessment, linguistic complexity and errors. System, 96, Article 102408. https://doi.org/10.1016/j.system.2020.102408. 10.1515/iral-2023-0223Sasaki, M., Mizumoto, A., & Matsuda, P. K. (2024). Machine translation as a form of feedback on L2 writing. International Review of Applied Linguistics in Language Teaching. https://doi.org/10.1515/iral-2023-0223. 10.5294/laclil.2016.9.1.6Enkin, E., & Mejias-Bikani, E. (2016). Using online translators in the second language classroom: Ideas for advanced-level Spanish. Latin American Journal of Content and Language Integrated Learning, 9(1), 138-158. https://doi.org/10.5294/laclil.2016.9.1.6. 10.1016/j.esp.2014.09.001Groves, M., & Mundt, K. (2015). Friend or foe? Google Translate in language for academic purposes. English for Specific Purposes, 37, 112-121. https://doi.org/10.1016/j.esp.2014.09.001. 10.5861/ijrsll.2019.4002O’Neill, E. (2019). Training students to use online translators and dictionaries: The impact on second language writing scores. International Journal of Research Studies in Language Learning, 8(2), 47-65. https://doi.org/10.5861/ijrsll.2019.4002. 10.1016/j.jslw.2024.101099Jiang, L., Yu, R., & Zhao, Y. (2024). Theoretical perspectives and factors influencing machine translation use in L2 writing: A scoping review. Journal of Second Language Writing, 64, Article 101099. https://doi.org/10.1016/j.jslw.2024.101099. Im, H. J. (2017). The university students’ perceptions or attitudes on the use of the English automatic translation in a general English class: Based on English writing lessons. Korean Journal of General Education, 11(6), 727-751. https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSe reArtiView.kci?sereArticleSearchBean.artiId=ART0022 99359 (Accessed: 02-05-24). 10.1002/tesq.3143Rowe, L. W. (2022). Google Translate and biliterate composing: Second-graders’ use of digital translation tools to support bilingual writing. TESOL Quarterly, 56(3), 883-906. https://doi.org/10.1002/tesq.3143. Darvin, R. (2023). Sociotechnical structures, materialist semiotics, and online language learning. Language Learning & Technology, 27(2), 28-45. https://hdl.handle.net/10125/73502 (Accessed: 02-05-24). 10.1016/j.system.2020.102302Jiang, L., Yu, S., & Wang, C. (2020). Second language writing instructors’ feedback practice in response to automated writing evaluation: A sociocultural perspective. System, 93, Article 102302. https://doi.org/10.1016/j.system.2020.102302. 10.1111/j.1944-9720.2006.tb02276.xWilliams, L. (2006). Web-based machine translation as a tool for promoting electronic literacy and language awareness. Foreign Language Annals, 39(4), 565-578. https://doi.org/10.1111/j.1944-9720.2006.tb02276.x.