The Application of Argumentation Theory to Translation Quality Assessment
Bibliographic record
Abstract
Translation quality assessment (TQA) models may be divided into two main types: (1) models with a quantitative dimension, such as SEPT (1979) and Sical (1986), and (2) non-quantitative, textological models, such as Nord (1991) and House (1997). Because it tends to focus on microtextual (sampling, subsentence) analysis and error counts, Type 1 suffers from some major shortcomings. First, because of time constraints, it cannot assess, except on the basis of statistical probabilities, the acceptability of the content of the translation as a whole. Second, the microtextual analysis inevitably hinders any serious assessment of the content macrostructure of the translation. Third, the establishment of an acceptability threshold based on a specific number of errors is vulnerable to criticism both theoretically and in the marketplace. Type 2 cannot offer a cogent acceptability threshold either, precisely because it does not propose error weighting and quantification for individual texts. What is needed is an approach that combines the quantitative and textological dimensions, along the lines proposed by Bensoussan and Rosenhouse (1990) and Larose (1987, 1998). This article outlines a project aimed at making further progress in this direction through the application of argumentation theory to instrumental 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 | 0.075 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".