Human-AI collaboration in translation and back translation of literary texts
In recent years, the significance of machine translation systems has grown due to the extensive production of online texts across various disciplines. Traditional translation methods have proven inadequate in meeting global translation needs. While translation tools are brilliant in addressing diver...
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| Published in: | Mağallaẗ al-dirāsāt al-iğtimāʻiyyaẗ (Online) Vol. 30; no. 2; pp. 173 - 192 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | Arabic English |
| Published: |
University of Science and Technology, Yemen
09-07-2024
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | In recent years, the significance of machine translation systems has grown due to the extensive production of online texts across various disciplines. Traditional translation methods have proven inadequate in meeting global translation needs. While translation tools are brilliant in addressing diverse disciplines and text genres, their usability and reliability face considerable debate, especially when applied to literary texts. Therefore, this research seeks to explore the impact of Artificial Intelligence (AI) translation tools (e.g., ChatGPT) on the translation and back translation of literary texts. The study employed an experimental model within a qualitative approach, utilizing a translation test as the primary research tool. 80 English-major students at Imam Mohammed Ibn Saud Islamic University (IMSIU) were randomly selected and assigned into four groups: two control and two experimental groups. Students are asked to translate and back translate an English short story and qualitative data from the test has undergone analysis through various comparisons. For statistical analysis, an independent samples t-test was employed to compare two independent groups. The findings revealed that students using AI tools were able to produce better translations and back translations than students using traditional methods, with slightly better performance in back translation. |
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| ISSN: | 2312-525X 2312-5268 |
| DOI: | 10.20428/jss.v30i2.2404 |