Pragmatic Loss In Ai And Human Translation Of Frankl’s Man’s Search For Meaning
Keywords:
AI translation, culture/domain-specific expressions, pragmatic translation strategies, pragmatic loss, Translation Quality AssessmentAbstract
Despite the immense potential of Artificial Intelligence (AI) translation models, they are still inadequate in dealing with culture/domain-specific expressions causing inaccurate translations. Moreover, human translators often excel AI translation models in translating these expressions accurately. The problem addressed in this study is that translating culture/domain-specific expressions by AI translation applications, i.e. Google Translate, ChatGPT, Deep L and Deep Seek result in pragmatic loss which distorts the intended meaning and function of the source text. The aim of this study is to bridge the gap in the literature concerning the competency of AI translation applications in rendering the said expressions from English into Arabic in Frankl’s Mans’s Search for Meaning, specifically in terms of the pragmatic loss. The researchers utilized a qualitative descriptive approach to analyze the data by adopting an eclectic approach. For the purpose of evaluation, certain parameters are selected, following Castilho et al. (2018). These parameters are adopted to evaluate the extent to which the selected AI translation applications have succeeded in rendering the source text to the target text. The study also adopts the numeric 5-point scale for measuring Fluency and Adequacy following Mauces and Donaj (2019). In addition, the pragmatic strategies for translating culture/domain-specific expressions are adopted from Chesterman (2016) to highlight which of these strategies are more effectively used in the AI translation applications than others. In addition, the researchers will offer alternative translations of the source text and in case the AI translation applications fail in providing the desirable versions. The findings of the study indicate that although some of the AI translation applications have succeeded in translating some of the ST expressions, however the rate of failure is still much higher. The AI translation applications have a lot of pragmatic losses and that they cannot replace human translation.
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