American Research Journal of Humanities and Social Sciences

The growing integration of translation into digital, scientific, commercial and other technology-driven domains has made the reliable assessment of translation quality increasingly important. Although numerous Translation Quality Assessment (TQA) models have been developed, questions remain regarding their practical applicability, technological integration, consistency of evaluation criteria, and ability to address the complexity of contemporary translation practices. This study critically examines existing approaches to TQA and identifies key gaps that limit their effectiveness in both academic research and professional translation settings. Particular attention is given to models that remain predominantly theoretical, lack sufficiently flexible assessment dimensions, or make limited use of emerging computational technologies. In response, the study presents a technology-oriented perspective for strengthening TQA through multidimensional evaluation, intelligent assessment techniques, improved corpus development, standardized quality indicators, and more accessible assessment tools. It further considers the potential of artificial intelligence and machine learning to support faster, more consistent, and context-sensitive evaluation of translated texts. By examining the strengths and limitations of different assessment approaches, the study develops a broader framework for understanding how technological innovation can complement human judgement rather than replace it. The findings offer practical insights for translators, educators, researchers, and technology developers seeking more reliable and adaptable approaches to contemporary translation quality evaluation.

Keywords: Translation Quality Assessment; Translation Evaluation Models; Artificial Intelligence; Machine Learning; Translation Technology; Corpus-Based Evaluation; Human–AI Collaboration.