Subtitle Translation Strategies as a Reflection of Technical Limitations: a Case Study of Ang Lee’s Films
Bibliographic record
Abstract
Subtitling, unlike traditional forms of translation, is subject to the limitations imposed by different subtitling apparatuses, for example, not more than two lines on one screen. In order not to breach these limitations, subtitlers adopt different strategies in their attempts to convey film plots or content to target language audiences, thereby creating an interface between culture and technology in the context of translation.This paper mainly looks at the interface which occurs in the process of translating film dialogue from Mandarin Chinese into English. Using as a case study films by Ang Lee, a prominent Chinese film director in global film circles, we shall focus on the investigation of translation strategies adopted in subtitling, and work out the possible interface between culture and technology in operation there. In addition, we may find an answer to the question whether technology is changing the face of translation.The film Wo Hu Cang Long [Crouching Tiger, Hidden Dragon] is the main case study considered and six of its English subtitle versions from China (including Hong Kong and Taiwan), America and the Great Britain respectively will be compared and discussed.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".