La traduction automatique comme outil d’aide à la rédaction scientifique en anglais langue seconde : résultats d’une étude exploratoire sur la qualité linguistique

Goulet, Marie-Josée; Simard, Michel; Escartín, Carla Parra; O’Brien, Sharon

doi:10.4000/asp.5045

Cited by 7 publications

(7 citation statements)

References 22 publications

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“…The result, however, is limited to the specific language pair between Korean and English. Different pairs of languages may lead to diverse results, as MT accuracy varies depending on language pairs (Goulet et al, 2017;Shadiev et al, 2019). Moreover, students across mother languages are prone to making idiosyncratic lexical and grammatical errors.…”

Section: Discussionmentioning

confidence: 99%

“…The units of analysis in this study were errors and changes that students produced at any level of symbol, word, phrase, or clause/sentence. The researchers first established error types based on the literature (Ferris, 2011;Goulet et al, 2017;Kim, 2019). Then, error types that distinctively and repetitively appeared in the Korean students' L2 texts were finalized through the reiterative processes of error analysis.…”

Section: Methodsmentioning

confidence: 99%

“…language, content, organization, and mechanics) and are limited by cognitive capacities such as attention and working memory (Waller & Papi, 2017). Thus, L2 writers are prone to producing more linguistic errors compared with L1 writers (Goulet, Simard, Parra Escartín & O'Brien, 2017). Therefore, revision is important for L2 writing, because without it, producing high-quality writing may be difficult for L2 students.…”

Section: Machine Translation and Error Correction In L2 Writingmentioning

confidence: 99%

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Effects of using machine translation to mediate the revision process of Korean university students’ academic writing

Lee

Briggs

2020

ReCALL

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In recent years, marked gains in the accuracy of machine translation (MT) outputs have greatly increased its viability as a tool to support the efforts of English as a foreign language (EFL) students to write in English. This study examines error corrections made by 58 Korean university students by comparing their original L2 texts to that of MT outputs. Based on the results of the error analysis, the error types were categorized into 12 categories. Students were divided into three distinctive groups to determine differences among them according to the frequency of errors in their writing. The t-test results reveal that the numbers of errors significantly decreased in the revised versions for most of the error types among all groups. The results of the regression analysis also reveal a positive correlation relationship between the number of changes and the reduction of errors. However, the results also indicate that although all groups made error corrections at similar rates, students who less frequently committed errors in their L2 texts (higher language proficiency groups) generally tended to correct a higher proportion of errors. Based on the findings, pedagogical implications are discussed regarding how EFL teachers can effectively incorporate MT into the classroom.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Machine Translation and Error Correction In L2 Writingmentioning

confidence: 99%

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Effects of using machine translation to mediate the revision process of Korean university students’ academic writing

Lee

Briggs

2020

ReCALL

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“…Using MT to support academic publication in English could be a solution to some of these problems. Indeed, if non-native authors have some mastery of English, which is more than likely the case for academics, they could write their papers in their L1, machine translate them, and then "self-post-edit" (MT+Self-PE; see Goulet et al 2017;O'Brien et al 2018).…”

Section: Academic Writing and Mt Literacymentioning

confidence: 99%

“…It may be that MT is a good solution for a 'first draft' that is not just post-edited, but used as a primer to help write a new draft. To date, there has been very little work on the cognitive challenges of using MT for self-publication (although see Goulet et al 2017;O'Brien et al 2018). Nurminen (2019; reports on the use of raw MT output by patent professionals for what she refers to as MT gisting to understand the contents of documents relevant to the patent process and international property rights that are in languages they have little or no competence in.…”

Section: Academic Writing and Mt Literacymentioning

confidence: 99%

MT Literacy—A cognitive view

O’Brien

Ehrensberger‐Dow

2020

TCB

Self Cite

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MT literacy means knowing how MT works, how the technology can be useful in a particular context, and what the implications are of using it for various purposes. As MT usage grows, the necessity for MT literacy also grows. This knowledge forms part of the greater need for digital literacies. In this contribution, we relate MT literacy to the concept of cognitive load in professional translation production and in translator training scenarios. We then move beyond the sphere of translation studies to examine other use-case settings – crisis communication, academic writing and patent publishing – to consider how MT can offer solutions and how MT literacy can impact cognitively in those settings. We discuss how training in MT literacy can empower language professionals and present two proposals for course content designed for MT users in other sectors.

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An investigation of machine translation output quality and the influencing factors of source texts

Lee¹

2021

ReCALL

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The use of machine translation (MT) in the academic context has increased in recent years. Hence, language teachers have found it difficult to ignore MT, which has led to some concerns. Among the concerns, its accuracy has become a major factor that shapes language teachers’ pedagogical decision to use MT in their language classrooms. Despite the urgency of the issue, studies on MT output quality in foreign language education remain scarce. Moreover, as MT is advancing every year, updated studies are imperative. Therefore, the present study investigated the quality of MT outputs (Google Translate) from Korean to English by comparing it with the English-translated texts of intermediate English as a foreign language students. The study also examined the factors within the source texts that affect the quality of MT outputs. Five trained evaluators examined multiple aspects of MT output samples (N = 104) and students’ English texts (N = 104), including mechanics, vocabulary, grammar, and context. The results showed that both texts were equally comprehensible, but MT outperformed the students in most aspects under investigation. The study further found that only two factors in the source texts – punctuation and sentence complexity – influenced MT output quality, whereas lexical and grammatical accuracy, lexical diversity, and contextual understanding did not affect it. Based on the results, the study presents classroom implications for using MT for educational purposes.

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La traduction automatique comme outil d’aide à la rédaction scientifique en anglais langue seconde : résultats d’une étude exploratoire sur la qualité linguistique

Cited by 7 publications

References 22 publications

Effects of using machine translation to mediate the revision process of Korean university students’ academic writing

Effects of using machine translation to mediate the revision process of Korean university students’ academic writing

MT Literacy—A cognitive view

An investigation of machine translation output quality and the influencing factors of source texts

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