Efficient Search for Equivalents at Your Fingertips – The Specialized Translator’s Dream
Bibliographic record
Abstract
The limitations of current terminology tools for specialized translators may, to a large extent, be explained by the complexity of the search process involved in producing good quality translations in specialist domains. This paper introduces a new approach to the development of this kind of resources aimed at satisfying the specific needs of specialized translators. This change of paradigm is reflected in the development of a prototype tool designed for use in legal translation. The tool – for use in English-Spanish translations of technological law in the localization of End User License Agreements – incorporates a revised corpus, comparative law information, and a terminological database. The features and advantages of the terminological database proposed are described in detail. Focusing on the specific needs of translators of this type of texts, comments are included on the acceptability of different terminological options on the basis of comparative legal analysis in different translation scenarios. The incorporation of these comments is a distinctive feature of this new approach to the development of resources and provides a value-added service to translators. The prototype tool designed is intended to serve as a model for the future development of similar applications in any type of specialized translation, in any given field and language combination.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.019 |
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".