EQUIVALENCE PROBLEMS FOUND IN MACHINE TRANSLATION OUTPUT: INSIGHTS FROM POST-EDITING ANALYSIS - Dwi Indarti; Vera Nurlia (Language Literacy: Journal of Linguistics, Literature, and Language Teaching, 2026)
This study examines post-editing strategies and equivalence problems in Google Translate (GT) outputs of a non-literary text, International Relations Theory by Ferreira (2017). Focusing on the English–Indonesian translation, the analysis is limited to five levels of equivalence proposed by Baker (2018): word, above-word, grammatical, textual, and pragmatic. Using a qualitative descriptive approach supported by LF Aligner, AntConc, and AntPConc, the study identifies equivalence problems in a parallel corpus. The findings reveal frequent word-level issues, particularly loanwords and lexical mismatches, requiring paraphrasing and cultural substitution. Problems also occur at the above-word and grammatical levels, including idiomatic expressions, collocations, singular–plural mismatches, and passive constructions. Textual issues relate to cohesion gaps, while pragmatic problems are less frequent but involve implicit meaning loss. Overall, GT performs better with high-frequency nouns, adjectives, and adverbs than with verbs. The study highlights the importance of post-editing machine translation (PEMT) and recommends its integration into translation curricula to enhance linguistic competence, digital literacy, and professional readiness in AI-assisted translation contexts.