<b>Computers and translation</b> : A translator’s guide. Ed. by Harold Somers. Amsterdam: John Benjamins, 2003. Pp. xvi, 351. ISBN 1588113779. $115 (Hb).
Bibliographic record
Abstract
Reviewed by: Computers and translation: A translator’s guide ed. by Harold Somers Shaoxiang Wang Computers and translation: A translator’s guide. Ed. by Harold Somers. Amsterdam: John Benjamins, 2003. Pp. xvi, 351. ISBN 1588113779. $115 (Hb). How can computers help translators and their profession? This is the question Harold Somers and other authors address in Computers and translation: A translator’s guide, developing the view that computers can become an essential tool that will make the translators’ job better. The seventeen chapters in this book fall roughly into two sections, with the first (Chs. 1–7) focusing on the uses of computers in translators’ work and the second (Chs. 8–17) on machine translation (MT). The first three chapters are contributed by Harold Somers. While the opening chapter, ‘Introduction’, sketches the history of MT and provides an overview of the volume, Ch. 2 describes the translator’s workstation as the ‘most cost-effective facility’ for translators (28). Ch. 3 takes a special look at translation memory. In ‘Terminology tools for translators’, Lynne Bowker draws our attention to a variety of terminology tools. Bert Esselink, in ‘Localisation and translation’, introduces the basics of localization and traces its history back to the early 1980s. In ‘Translation technologies and minority languages’, Somers takes up the issue of computer-aided translation (CAT) and minority languages and projects its development. Sara Laviosa’s ‘Corpora and the translator’ outlines some of the current and potential uses of corpora in the empirical study of translation, translator training, and professional training. The remainder of the volume focuses more on MT. In ‘Why translation is difficult for computers’, Doug Arnold looks closely at the difficulties involved in MT in the light of the nature of translation. In ‘The relevance of linguistics for machine translation’, Paul Bennett considers some rigorous and systematic ways in which linguistics can be of use in MT systems. W. John Hutchins, in ‘Commercial systems: The state of the art’, reports on the current status and potentials of commercial MT systems and translation tools. In ‘Inside commercial machine translation’, Scott Bennett and Lauri Gerber explore commercial MT systems from the developer’s point of view. In ‘Going live on the internet’, Jin Yang and Elke Lange demystify the first free online translation service. In ‘How to evaluate machine translation’, John S. White [End Page 544] highlights the importance of evaluation in MT and, more importantly, alerts the researcher to its pitfalls. Eric Nyberg, Teruko Mitamura, and Willem-Olaf Huijsen, in ‘Controlled language for authoring and translation’, explain how controlled language can be applied to MT to ensure better quality output. In ‘Sublanguage’, Somers, also aiming at getting the best out of MT, discusses a successful sublanguage MT system—the Canadian Météo system—and analyzes its implication for future MT development. In ‘Post-editing’, Jeffrey Allen discusses the relevance, importance, and characteristics of post-editing. Finally, in ‘Machine translation in the classroom’, Somers considers the application of MT and CAT tools to the teaching of translation. Focusing on practical and usable MT and CAT tools, this volume should be of interest to anyone interested in language and translation. Shaoxiang Wang Fujian Teachers University Copyright © 2005 Linguistic Society of America
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.191 | 0.220 |
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".