Mapping The Landscape of Machine Translation Error Research: A Bibliometric Review (1980-2023) - Siregar, Sri Dewi Priwarti; Al Farisi, Mohamad Zaka; Naser, Abdurrahman Do. Muhamad (Fakultas Adab and Humaniora, Universitas Islam Negeri (UIN) Syarif Hidayatullah Jakarta, 2024)
PurposeDespite machine translation enhances communication process, it faces problems to guarantee accurate translation. Efforts to address these problems have been done by many researchers. The study aimed to reveal a comprehensive bibliometric review considering research trends, publishing contributions, and development in 1980-2023.MethodThis qualitative study made uses of a descriptive research design engaging some research on machine translation errors. This study employed bibliometric analysis to comprehensively examine the landscape of previous research on machine translation errors from 1980 to 2023. Based on a Scopus database, 138 publications were initially identified and subsequently refined to 98 articles that were directly relevant to the research topic. The bibliometric analysis highlighted the intriguing patterns and trends in this field.Results/FindingsThe findings revealed that the year 2021 witnessed the highest number of article publications, totaling 13 articles, indicating a growing interest in the topic. Moreover, a notable citation trend emerged in 2011, with 103 citations, signifying the significance and influence of research related to translation errors in machine translation. China notably emerged as the leading country to publish articles on this subject, with 23 publications and 28 collaborative links established with other countries. Among the 98 journals that published research in this domain, 45 of them were classified as Q1 journals, signifying their high impact and scholarly reputation.ConclusionThe three main aspects comprising errors and human factors, exploration of machine translation, machine learning, and translation languages, and investigations within the field of computational linguistics collectively contributed to a deeper understanding of the complexities and challenges associated with translation errors in machine translation.