A Multidimensional Analysis of Human and ChatGPT-Generated English Translations of Arabic Film
Bibliographic record
Abstract
This study analyzed lexico-grammatical variations between two text types: human-written and machine-generated, using Biber's multidimensional analysis. It explores the effectiveness and limitations of AI-driven translation systems in maintaining the quality of film translations. It aims to add to the current discussion on the impact of AI in the field of translation. The research methodology involves selecting films from the Middle East and collecting their translations, both human-written and generated by ChatGPT. Biber's multidimensional analysis framework analyses the translations across dimensions such as involved versus informational discourse, narrative versus non-narrative concerns, explicit versus situation-dependent, overt expression of argumentation/ persuasion, and abstract versus non-abstract discourse. The findings of the analysis reveal similarities and differences between human and ChatGPT translations. Human translations are more involved, situation-dependent, argumentative, non-abstract, and less non-narrative than the translations generated by AI. However, further improvements and refinements in AI translation models could help bridge the gap between human and AI translations. The results gained from this comparative analysis offer insight into improving AI-driven translation systems, leading to more effective cross-cultural communication through film. This research will potentially contribute to the advancement of the field of translation studies by bridging the gap between human and AI translations. It provides valuable implications for the future development of AI technologies in film translation.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".