Emerging Research Trends in Natural Language Processing for Multilingual AI
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Abstract
This study explores the emerging trends and developments in Natural Language Processing (NLP) for Multilingual Artificial Intelligence (AI) through a comprehensive bibliometric analysis. Drawing on data from the Scopus database spanning 2013 to 2023, the research identifies key publication patterns, influential contributors, thematic clusters, and collaboration networks that shape the evolution of multilingual NLP. The analysis reveals a significant increase in research activity over the past five years, particularly driven by advancements in deep learning and the emergence of multilingual pretrained models such as mBERT and XLM-RoBERTa. Institutions from the United States, India, and China lead the global research landscape, while collaborative clusters highlight the interdisciplinary and international nature of the field. Keyword analysis shows a paradigm shift from rule-based and statistical approaches to neural and transformer-based architectures, with increasing application in healthcare, social media, and big data environments. Despite this growth, the study identifies ongoing challenges, including disparities in language representation, bias in model training, and the need for ethical and inclusive research practices. The findings provide a strategic overview for researchers, policymakers, and practitioners aiming to advance equitable and effective multilingual AI systems.
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