Assessing DeepSeek and ChatGPT in English-Arabic translation of political texts
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Abstract
This study compares the performance of two state-of-the-art AI translation models, DeepSeek and ChatGPT. With a focus on the accuracy and fluency criteria, the research explores how the two models translate English political news headlines into Arabic. Data were extracted from ten English-language news websites: Reuters, BBC, Al Jazeera, The Guardian, The Wall Street Journal, and The Times, which varied in their reporting styles and commentary regarding the events of 2024. Very politicized content was involved in these samples, which encompassed the DeepSeek and ChatGPT models that were trained. Translations were subsequently subjected to rigorous testing by the Multidimensional Quality Metrics (MQM) system, which assessed accuracy, fluency, terminology, style, and the effective handling of context and nuance. Comparison demonstrates that both models have particular strengths and weaknesses. AI translation engines must be tuned and tested in a way that ensures responsible and dependable cross-lingual communication, especially in political news reporting. The findings provide a deeper understanding of the promise and limitations of AI in enabling next-generation political communication, as well as the extent to which further research is needed to develop such technology.
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