Error annotation and analysis of a (semi-)specialised English-French learner translation corpus
Bibliographic record
Abstract
This study aims to identify students’ main difficulties when translating (semi-)specialised texts on the basis of an error-annotated English-French learner translation corpus. Students are M1 students in a translation training programme. The corpus contains two textual genres, namely research articles and popularisation articles. We use the same translation error annotation scheme for both text types as well as a corpus-based analysis. We then aim to identify and compare the difficulties students encounter based on genres and register. The results show that the translations of popularisation texts contain more content transfer errors, which may be due to students’ tendency to take more risks when translating these, as they may feel more confident. They also show that the translations of popularisation texts contain fewer terminology errors, most probably due to lower terminological density which characterises this type of text. However, our study also shows that these differences prove to not be statistically significant, which implies that content transfer errors and terminology errors are as important in specialised as in popularisation articles translations.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".