Machine translation and foreign language education

Urlaub, Per; Dessein, Eva

doi:10.3389/frai.2022.936111

Cited by 29 publications

(10 citation statements)

References 21 publications

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“…In addition to recognizing the limitations of modern technology, educators must help learners of all levels interacting with machine translators to recognize these limitations. A language class that integrates machine translators should also provide students with an opportunity to learn about the limitations of machine translators (Urlaub & Dessein, 2022 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence Technologies in College English Translation Teaching

Wang

2023

J Psycholinguist Res

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This paper explores the practical prospects for using artificial intelligence technologies in professional English-speaking translator education. At the online conference ‘Translation Skills in Times of Artificial Intelligence’ (DingTalk platform, January 2022), the teachers of higher education institutions in China prioritized the translator’s competencies necessary for successful professional activity during the digital transformation of social and economic business relations. The educators also evaluated the demand for online services used in the education of English–Chinese interpreters. The survey results showed that the use of artificial intelligence technologies in educational practices could have a significant impact on the development of key competencies of future translators. Using a competency-based approach to interpreter training and considering the need to develop abilities, knowledge, and skills required for successful professional translation activity, the author developed the pedagogical concept of the online educational course ‘Simultaneous and asynchronous translation in a digital environment.’

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Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence Technologies in College English Translation Teaching

Wang

2023

J Psycholinguist Res

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“…Unlike the biomedical sciences, a large amount of data on machine translation is available in the field of educational linguistics, second language studies, and foreign language education. Two review articles have recently been published [ 28 , 29 ]. These papers summarized the key concepts, insights, and findings, categorizing them into questions like how learners use MTs, what instructors and learners think about MTs, and how MTs affect language learning.…”

Section: Discussionmentioning

confidence: 99%

Performance of machine translators in translating French medical research abstracts to English: A comparative study of DeepL, Google Translate, and CUBBITT

Sebo,

de Lucia

2024

PLoS ONE

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Background Non-English speaking researchers may find it difficult to write articles in English and may be tempted to use machine translators (MTs) to facilitate their task. We compared the performance of DeepL, Google Translate, and CUBBITT for the translation of abstracts from French to English. Methods We selected ten abstracts published in 2021 in two high-impact bilingual medical journals (CMAJ and Canadian Family Physician) and used nine metrics of Recall-Oriented Understudy for Gisting Evaluation (ROUGE-1 recall/precision/F1-score, ROUGE-2 recall/precision/F1-score, and ROUGE-L recall/precision/F1-score) to evaluate the accuracy of the translation (scores ranging from zero to one [= maximum]). We also used the fluency score assigned by ten raters to evaluate the stylistic quality of the translation (ranging from ten [= incomprehensible] to fifty [= flawless English]). We used Kruskal-Wallis tests to compare the medians between the three MTs. For the human evaluation, we also examined the original English text. Results Differences in medians were not statistically significant for the nine metrics of ROUGE (medians: min-max = 0.5246–0.7392 for DeepL, 0.4634–0.7200 for Google Translate, 0.4815–0.7316 for CUBBITT, all p-values > 0.10). For the human evaluation, CUBBITT tended to score higher than DeepL, Google Translate, and the original English text (median = 43 for CUBBITT, vs. 39, 38, and 40, respectively, p-value = 0.003). Conclusion The three MTs performed similarly when tested with ROUGE, but CUBBITT was slightly better than the other two using human evaluation. Although we only included abstracts and did not evaluate the time required for post-editing, we believe that French-speaking researchers could use DeepL, Google Translate, or CUBBITT when writing articles in English.

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“…The study argues the necessity for language teachers to continually adapt and improve their teaching strategies to incorporate these advanced tools effectively. Urlaub and Dessein (2022) discuss the transformative potential of online MT tools in foreign language education, emphasizing the need for pedagogical tools based on MT that acknowledge and can handle socio-culturally complex source texts. They advocate for research and development priorities that focus on optimizing support for language learners through technology.…”

Section: Literature Reviewmentioning

confidence: 99%

The Roles of Machine Translation in Learning Among K-12 Students at the International Schools in the Great Bay Area, China

Chen,

Liu,

et al. 2023

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Intercultural foreign language learning among K -12 students at international schools involves the cross-cultural discovery of the changing habits of people whose mother tongue might be different instructive languages at schools. Bilingual education is essential in bridging the gap between the mother tongue and instructive languages. However, it needs effective strategies that combine in-classroom language learning with new technology. Several language learning and teaching trends concerning the digital environment and innovative technologies such as machine translation or digitalization are currently challenging K-12 schools and families. This research investigates the role of machine translation in learning among K-12 students at international schools in the cities of Shenzhen, Guangzhou, and Zhuhai. It aims to identify these gaps in existing research and propose innovative strategies for boosting learning outcomes to recognize the learning habits they developed as young learners in the 21st century and their cultural motivations. Bilingual surveys are being handed out to K-12 families. These surveys are written in both English and Chinese. In total, 221 of them were collected. The data was analyzed to examine how helpful machine translation is for academic learning in both languages. The strengths and weaknesses were summarized among K-12 students using machine translation, highlighting strategies used with it at international K-12 schools.

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Machine translation and foreign language education

Cited by 29 publications

References 21 publications

Artificial Intelligence Technologies in College English Translation Teaching

Artificial Intelligence Technologies in College English Translation Teaching

Performance of machine translators in translating French medical research abstracts to English: A comparative study of DeepL, Google Translate, and CUBBITT

The Roles of Machine Translation in Learning Among K-12 Students at the International Schools in the Great Bay Area, China

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