Strategies of Rendering Metaphor from Arabic into English: A Comparative Study of ChatGPT and Matecat
Bibliographic record
Abstract
This study examines the translation strategies employed in rendering Arabic metaphors from Naguib Mahfouz's Zuqāq al-Midaq into English, comparing the approaches of a human translator with those of two machine translation systems: ChatGPT-4 and Matecat. Drawing upon Nida's (1964) theory of formal and dynamic equivalence, the research analyzed 106 metaphors to identify predominant translation strategies. The findings reveal that the human translator consistently adopted dynamic equivalence, effectively re-creating the metaphorical meaning and cultural nuances for the target audience. In contrast, while ChatGPT-4 demonstrated a notably higher tendency towards dynamic equivalence compared to Matecat, both machine translation systems still frequently resorted to formal equivalence, resulting in more literal or less idiomatic renditions. This indicates that, despite advancements, machine translation, including advanced large language models (LLMs), continues to face significant challenges in accurately conveying the subtle complexities of figurative language. The study highlights the indispensable role of human translators in achieving nuanced and culturally sensitive metaphorical translations while also underscoring the potential of advanced AI tools to enhance the translation process when complemented by human expertise.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".