<![CDATA[The effectiveness of register-based machine translation (MT) is assessed in this research using text categorization methodologies. Given that different registers call for different translation strategies—formal, informal, academic, or conversational—the objective of this study is to assess how well MT systems adapt to various registers. A dataset of texts from different domains that had been translated using an MT engine was classified using supervised machine learning methods to determine register-specific correctness and appropriateness. The evaluation focuses on linguistic features, translation accuracy, and register consistency. The results demonstrate that register-aware MT significantly improves translation quality and contextual relevance, especially in the academic and professional domains. The findings show how text classification may be integrated into MT evaluation frameworks to enhance output quality and guide future system development. This supports the register itself as one of the essential components that must be included in register-based machine translation assessment.]]>
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