A Bibliometric Overview of Translation Research Across Disciplines in India

Abstract

Driven by the integration of machine learning and natural language processing techniques, translation research in India has developed significantly since 2010. As machine learning introduces new and innovative aspects of algorithms to machine translation, this article aims to map the evolving trends in translation research across disciplines in India. This study uses bibliometric methods to track the landscape of translation research over the past fifty years using data scraped from Scopus and Web of Science databases. To identify the research themes and clusters, a dataset consisting of 2167 screened data was assessed by employing performance analysis and science mapping techniques. The findings reveal a marked paradigm shift in research themes of translation research, i.e, from traditional mathematical models to advanced neural machine translation and deep learning. This transition highlights the dominance of machine translation in STEM disciplines and their research affiliations within premier institutes of India. By outlining the research patterns in translation studies, this article provides critical insights into the interdisciplinarity of translation as a research domain and its growth in the Indian academia. © 2026, DESIDOC.