ADVANCING AI TRANSLATION RESEARCH: TRENDS IN TOP-TIER INTERNATIONAL APPLIED LINGUISTICS JOURNALS

Abstract

The complexities surrounding AI translation in applied linguistics represent a critical challenge in the advancement of language technologies in the 21st century. This research applied content analysis to several articles published in top-tier international applied linguistics journals from 2015 to 2024, focusing on AI translation as the central theme. The study reveals a substantial growth in publications on this subject, particularly over the last four years. Research and development (R&D) has been identified as the central theme, with translation methodologies and assessment frameworks serving as the primary areas of inquiry. The studies exhibit a wide thematic scope, with Artificial Intelligence (AI) and machine learning emerging as the most frequently employed technologies. Among the methodologies, machine translation (MT) and natural language processing (NLP) were the most prominent, while coding sheets and narrative analysis were the preferred instruments for data collection and evaluation. Drawing on these findings, several recommendations are proposed
to improve the effectiveness of AI translation. Future research should prioritize the development of comprehensive evaluation frameworks that combine quantitative and qualitative methodologies, emphasizing both linguistic precision and the cultural and contextual nuances of language. Additionally, enhancing the quality and diversity of datasets is essential to achieving broader and more inclusive linguistic representation.