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The Quality and Accuracy of AI-Generated Translation in Translating Communication-Based Topics: Bringing Translation Quality Assessments Into Practices
Abstract
This chapter presents the efficacy and obstacles associated with the quality of AI-generated translation, concentrating on instruments such as ChatGPT and Neural Machine Translation (NMT) technology. It emphasizes the superiority of NMT systems over traditional rule-based and statistical methodologies by employing deep learning techniques to understand language contextually, thereby yielding translations that are both more accurate, acceptable, and readable. Nonetheless, considerable challenges persist, particularly in navigating cultural subtleties, idiomatic phrases, and intricate linguistic frameworks. By using a Translation Quality Assessment (TQA), this chapter appraises ChatGPT's translation capabilities based on three criteria: accuracy, acceptability, and readability. Although ChatGPT exhibits commendable performance in terms of general linguistic accuracy and readability, it encounters difficulties regarding acceptability when confronted with culturally rich or highly contextualized texts, especially within specialized domains such as political communication discourses.
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