Assessing Chinese-to-English Translation Quality – A Systemic Functional Linguistics Perspective

Abstract

Translation quality assessment is an important issue in translation teaching and learning, but it has been under-researched in translation studies. Whether a translation is good or not depends largely on a translator’s ability of text analysis. Taking the translation task of TEM8 (Test for English Majors Band 8) in 2023 as an example, this paper presents a pilot project aimed at exploring a systematic way of analyzing translation errors by referring to systemic functional linguistics (SFL). In particular, the paper investigates how SFL-based text analysis of ideational meaning, interpersonal meaning and textual meaning can be used for translation teaching and learning, through comparative analyses of a set of texts, including a Chinese source text, two translation texts from TEM8 in 2023, and an AI-generated literal translation as back translation. The study finds that it is possible to identify, describe and classify translation errors in the translated texts, and more significantly, the resulting error description and classification allows translation teachers a more precise expression of the nature of poor translation or translation errors that would otherwise be simply put as “inadequate” or “awkward” translation, and students a more tangible understanding of what counts as an “excellent” translation. Following the analyses, the paper discusses the pedagogical effects of SFL-based text analysis by conducting a survey and semi-structured interviews with students. The quantitative data show that overall, students held a positive attitude towards translation, and the qualitative data analysis uncovers specific benefits and challenges experienced by the students.