Web-based translation for promoting language awareness

Abraham, Lee B.

doi:10.1075/lllt.25.06abrl

Cited by 4 publications

(4 citation statements)

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“…Regarding the first research question, the results revealed that MT generated more qualified English texts than the students in most aspects under investigation. Previous studies showed that MT achieved lower accuracy compared to intermediate human translators and is predisposed to produce more lexical and grammatical errors (Abraham, 2009;Fredholm, 2015;White & Heidrich, 2013). In contrast, the current study, as also claimed in the most recent studies (Briggs, 2018;Ducar & Schocket, 2018;Stapleton & Kin, 2019;Tsai, 2019), indicated a significant improvement in the quality of the MT outputs in terms of lexicon and grammar and, as a result, outperformed the intermediate EFL students in almost all investigated aspects.…”

Section: Credibility Of Mt Outputssupporting

confidence: 54%

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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Section: Credibility Of Mt Outputssupporting

confidence: 54%

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

Lee¹

2021

ReCALL

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“…Despite the substantial progress made in translation technology over the years, it is still challenging to produce error-free texts using MT, and frequent errors have been reported in grammar (Groves & Mundt, 2015;Niño 2009), register (formal vs. informal) and cultural references (Correa, 2014;Niño 2009), colloquial language (Luton, 2003), pragmatic usage that convey subtle meaning (Ducar & Shocket, 2018), polysemic words (Abraham, 2009) and proverbs and idioms (Correa, 2014;Kim, 2018;Luton, 2003). In translations between Korean and English more specifically, Chang (2018) reported that MT shows general weaknesses in tag-questions, echo-questions, gerunds, NP-movements, relative pronouns, the passive voice, and polysemic words.…”

Section: Limitations and Benefits Of Mtmentioning

confidence: 99%

“…When the English texts were compared and analyzed using online computational assessments, MT English texts were evaluated to be higher in quality in grammar, spelling, and word choice leading the author to conclude that MT can indeed provide effective support in EFL writing. In fact, educational activities such as analyzing mistranslations, preediting, and post-editing were found to help develop meta-linguistic awareness by directing students' attention to the differences and similarities between their native language and their L2 (Abraham, 2009;Correa, 2014;Enkin & Mejías-Bikandi, 2016;Niño, 2009;Somers, 2003). O'Neill (2012O'Neill ( , 2016 found that such activities and training in the use of MT enable learners to write more accurately and receive higher subscores on L2 writing tasks (comprehensibility, spelling, content, and grammar) than those who did not.…”

Section: Limitations and Benefits Of Mtmentioning

confidence: 99%

The Effect of L2 Proficiency on Post-editing Machine Translated Texts

Chung¹

2020

The Journal of AsiaTEFL

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The present study examines how proficiency in the second language (L2) affects language learners' ability to post-edit texts that are machine-translated from the learners' native language (L1) to L2. More specifically, the study investigates how L2 proficiency level affects the degree to which Korean learners of English can discern the accuracy of L1-to-L2 machine-translated (MT) text and whether it affects the level of errors (word, phrase, clause, sentence) that are corrected in the post-editing process. Fifty-nine Korean university students' proficiency was measured using a cloze test and a writing test. They were then given a source text (Korean) and a machine-translated text (English) and were asked to detect and correct as many errors as they can. The overall results of the study showed that L2 proficiency does have a significant effect on how language learners post-edit machine-translated output. With increasing proficiency, the number of corrections increased especially above the wordlevel, and significant group differences could be found in post-editing patterns of the MT text. The findings have important pedagogical implications for integrating MT activities in L2 classrooms with learners of different proficiency levels.

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“…For instance, MT can increase writing fluency and decrease errors (Garcia & Pena, 2011), promote lexical fluency (Chen, Huang, Chang & Liou, 2015), and can be employed as corpora that cater to students' needs in the language classroom (Bernardini, 2016). Additionally, an important advantage of MT is the provision of feedback to L2 writers, providing assistance to correct mistakes that can take multiple forms, including syntactic alternatives and word choice (Lee, 2020), thus raising language learners' metalinguistic awareness (Abraham, 2009).…”

Section: Introductionmentioning

confidence: 99%

Genre effect on Google Translate–assisted L2 writing output quality

Alrajhi¹

2022

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This study investigates and compares the quality of Google-translated texts (GTTs) across writing genres (narrative, descriptive, expository, and persuasive) with EFL student–generated texts (SGTs), and explores students’ attitudes toward Google Translate (GT) output. In a mixed-methods design with a computational approach to text analysis, this study utilizes multiple data sources, including 328 written texts, written reflections, four attitude questionnaires, focus group discussion, and individual interviews. Forty-one Arabic-speaking undergraduate students majoring in English language and translation at a Saudi university participated in this study. They engaged in eight computer-mediated writing sessions by responding to tasks using English, then responding to the same tasks using their first language (L1). Subsequently, they utilized GT to translate L1 texts into English and compared SGTs with GTTs. The findings show that GTTs have higher literacy levels and richer content in the persuasive and expository genres, and higher style levels in the narrative and descriptive genres. Moreover, the comparison between SGTs and GTTs reveals that GTTs have higher literacy levels, better style, and richer content in the descriptive, expository, and persuasive genres. Meanwhile, the students hold positive views on the general quality, grammatical accuracy, and provision of lexical alternatives in GTTs across genres. Pedagogical implications are discussed.

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Web-based translation for promoting language awareness

Cited by 4 publications

References 0 publications

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

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

The Effect of L2 Proficiency on Post-editing Machine Translated Texts

Genre effect on Google Translate–assisted L2 writing output quality

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