Assessing Chinese-to-English Translation Quality – A Systemic Functional Linguistics Perspective
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".