The automatic translation of film subtitles: a machine translation success story?
Bibliographic record
Abstract
Every so often one hears the complaint that 50 years of research in Machine Translation (MT) has not resulted in much progress, and that current MT systems are still unsatisfactory. A closer look reveals that web-based general-purpose MT systems are used by thousands of users every day. And, on the other hand, special-purpose MT systems have been in long-standing use and work in particular domains or for specific companies. This paper investigates whether the automatic translation of can be considered machine translation success story. We describe various projects on MT of and contrast them to our own project in this area. We argue that the text genre film subtitles is well suited for MT, in particular for Statistical But before we look at the translation of let us retrace some other MT success stories. Hutchins (1999) lists number of successful MT systems. Amongst them is Meteo, system for translating Canadian weather reports between English and French which is probably the most quoted MT system in practical use. References to Meteo usually remind us that this is highly constrained sublanguage system. On the other hand there are general purpose but customer-specific MT systems like the English to Spanish MT system at the Pan American Health Organization or the PaTrans system which Hutchins (1999) calls ... possibly the best known success story for custom-built MT. PaTrans was developed for LingTech A/S to translate English patents into Danish. Earlier Whitelock and Kilby (1995) (p.198) had called the METAL system a success story in the development of MT. METAL is mentioned as successfully used at number of European companies (by that time this meant few dozen installations in industry, trade or banking). During the same time the European
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".