A Study of the Translation of English and Chinese Film Titles From the Perspective of Culture and the Beauty of Language
Bibliographic record
Abstract
In recent years, with the fast development of our material and spiritual life, film is becoming an important way of our entertainment and relaxation. Chinese films emerge unceasingly, at the same time, foreign films are pouring into our Chinese film market unceasingly, especially the Hollywood films, which bring us infinite enjoyment. What’s more, Chinese excellent films are introduced to foreign countries as well，praised and loved by foreign audience, which makes Chinese very proud of that. Film, which occurs in the 19th century in Europe, is now loved and highly praised by people all over the whole world. A good film can not only make us enjoyable mentally, but also stimulate and guide our behaviors to some extent. While a film title, being a symbol of a film, becomes the key to catch the eyeballs of the audience. Furthermore, it is the key to make audience remember the film. Therefore, the translation of film title has aroused great interests of translators. How to translate a film title from English to Chinese or Chinese to English well, and keep the originality maximally becomes the major research project of translators. Although being studied all the time, and having made a great achievement, there are no systematic translating methods and principle yet. Our research is far from enough. Verchueren thought that culture is an important element in social world. It has a great influence on both our behaviors and languages. While the aesthetic and vocative function of language proposed by Newmark has a great influence on our mental world. Therefore, a good film title translation is based on the study of the culture and the beauty of language. The thesis studies how the film title translation is influenced by the culture and beauty of language through plentiful examples. The combination of the two elements that influence film title translation will make the translation more remarkable. Thus to catch the eyeballs of audience, which has some practical values.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".