Managing Terminology for Translation Using Translation Environment Tools: Towards a Definition of Best Practices
Bibliographic record
Abstract
Translation Environment Tools (TEnTs) became popular in the early 1990s as a partial solution for coping with ever-increasing translation demands and the decreasing number of translators available. TEnTs allow the creation of repositories of legacy translations (translation memories) and terminology (integrated termbases) used to identify repetition in new source texts and provide alternate translations, thereby reducing the need to translate the same information twice. While awareness of the important role of terminology in translation and documentation management has been on the rise, little research is available on best practices for building and using integrated termbases. The present research is a first step toward filling this gap and provides a set of guidelines on how best to optimize the design and use of integrated termbases. Based on existing translation technology and terminology management literature, as well as our own experience, we propose that traditional terminology and terminography principles designed for stand-alone termbases should be adapted when an integrated termbase is created in order to take into account its unique characteristics: active term recognition, d one-click insertion of equivalents into the target text and document pretranslation. The proposed modifications to traditional principles cover a wide range of issues, including using record structures with fewer fields, adopting the TBX-Basic’s record structure, classifying records by project or client, creating records based on equivalent pairs rather concepts in cases where synonyms exist, recording non-term units and multiple forms of a unit, and using translated documents as sources. The overarching hypothesis and its associated concrete strategies were evaluated first against a survey of current practices in terminology management within TEnTs and later through a second survey that tested user acceptance of the strategies. The result is a set of guidelines that describe best practices relating to design, content selection and information recording within integrated termbases that will be used for translation purposes. These guidelines will serve as a point of reference for new users of TEnTs, as an academic resource for translation technology educators, as a map of challenges in terminology management within TEnTs that translation software developers seek to resolve and, finally, as a springboard for further research on the optimization of integrated termbases for 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 | 0.087 | 0.089 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.038 | 0.050 |
| Open science | 0.014 | 0.017 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".