Efficient Search for Equivalents at Your Fingertips – The Specialized Translator’s Dream
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it