Theory and Practice in Language Studies · 2024 · 34 citations · 18 references
HumanitiesArabicTranslation StudiesCorpus-based Translation StudiesTechnical TranslationTranslation TheoryComparable Corpus AnalysisComparative Translation AnalysisMachine TranslationTranslation CorporaTranslation Quality AssessmentTranslation TechnologyAutomated Translation EvaluationBilingual Evaluation UnderstudyComparable Corpus DesignHuman Evaluation Of Machine TranslationMachine Translation ComparisonMachine Translation Quality EvaluationTranslation Quality BenchmarkingTranslation Quality Metrics
This study evaluates the aptitude of ChatGPT for Arabic-English machine translation. The main objective of this research is to scrutinize the quality of ChatGPT's translations and compare its performance against machine translation systems, such as Google Translate, which are intricately tailored for translation purposes. In addition, the study seeks to investigate the potential integration of ChatGPT into translation workflows. Furthermore, we aspire to discern whether ChatGPT's translation efficacy harmonizes with or diverges from the profound finesse exhibited by human translation expertise. To accomplish this, a comparable corpus of 1000 English sentences and their corresponding Arabic translations was employed to evaluate the translation outputs of both machine translation systems alongside a human translation reference. The corpus was sourced from Tatoeba, an open online platform and underwent electronic assessment using the BLEU (Bilingual Evaluation Understudy) metric. The results indicate a marginal advantage of ChatGPT over Google Translate in delivering high-quality translations. Upon evaluating the corpus, we ascertain that ChatGPT performs impressively well compared to specialized translation systems like Google Translate. However, despite these promising findings, it is essential to acknowledge that even the most advanced machine translation technology, ChatGPT, cannot currently match the proficiency of human translation, at least not in the near future.
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