Evaluation briefs: drawing on translation studies for human evaluation of MT
Bibliographic record
Abstract
In this position paper, we examine ways in which researchers in machine translation and translation studies have approached the problem of evaluating the output of machine translation systems and, more broadly, the questions of what it means to define translation quality. We explore their similarities and differences, highlighting the role that the purpose and context of translation plays in translation studies approaches. We argue that evaluation of machine translation (e.g., in shared tasks) would benefit from additional insights from translation studies, and we suggest the introduction of an ‘evaluation brief” (analogous to the ‘translation brief’) which could help set out useful context for annotators tasked with evaluating machine translation.
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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 | 0.259 | 0.451 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.028 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".