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

Subject Areas : All areas of language and translation

Mohammad Iman Askari ¹ (Assistant Professor, English Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran)
Adnan Satariyan ² (Director, Standardization and Quality Assurance Department, UAE Branch, Islamic Azad University, Dubai, United Arab Emirates)
Mahsa Ranjbar ³ (PhD Candidate, English Department, Faculty of Persian Literature and Foreign Languages, Allameh Tabataba’i University, Tehran, Iran)

Received: 2021-07-25 Accepted : 2021-07-25 Published : 2021-07-01

Keywords: Text, errors, Google Machine Translate (GMT), Translation Quality Assessment (TQA),

Abstract :

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.

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