English Translation of Verbal Humour in Egyptian Comedy Films
Bibliographic record
Abstract
Humour is deeply rooted in culture and may differ remarkably across societies. Translating humour interlingually necessitates navigating cultural variations and references, mainly because humour often relies on language-specific elements such as puns, idiomatic expressions and wordplay. Translating these linguistic features while preserving the comedic effect can be particularly challenging. This study investigates the complexity of translating humour that depends on a combination of both cultural and linguistic elements involving play with words and sounds from the Egyptian Arabic vernacular into English. To explore this area, the researchers examine three films in which humour is deemed by viewers as unique. The verbal humour investigated depends heavily on the replacement of words and sounds in a vast array of expressions that include puns, irony, jokes, spoonerisms, malapropisms, collocations, and proverbs. The results of the analysis of 34 examples extracted from the three films demonstrate that the translators of the films rejected several puns in the sense that they disregarded the translation of most puns, while resorting to communicative translation with some other puns. However, with other linguistic humorous devices such as malapropisms, irony, jokes, and spoonerisms, the translator used strategies including explicitation, transposition, literal translation (calque), and omission.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".