Investigating Covert and Overt Errors Using Machine Translation according to House’s (2015) TQA Model within Academic Context

The current study investigated Persian-English translations conducted by a human translator and a machine translator. The researchers employed House’s Translation Quality Assessment (TQA) model to evaluate the differences between the two translated works. Accordingly, they had the Persian texts translated by a human translator and Google Machine Translator (GMT). The translation quality, error recognition, and mismatches of the two translations were subsequently analyzed. The results showed a one-to-one match between the source and target texts regarding the human translator’s work. Furthermore, the results revealed both overt and covert errors when comparing the human and machine translators. The error analysis results also suggested that the quality of the output provided by the GMT can cause misunderstanding in the meaning. Academic texts could mean different in various contexts. Hence, it is necessary to consider human interferences when dealing with the genre of the academic text.