Bibliographic record
Abstract
Translation is an endeavour to automate all or part of the process of translation from one language to another. In general, translation is a complex task, which aims at preserving the semantic and stylistic equivalents of the source language texts into the target language texts. The most problematic area in manual and machine translation is the lexicon and the role it plays according to the context to create deviations. There are also cases where deviations occur owing to the divergence – one of the complex areas of investigation in translation. Divergence in translation normally arises when the sentences in the source language are realized differently in the target language. This paper seeks to discuss some of the major divergences that observed in English to Bengali translation. It also investigates how the different linguistic and extralinguistic constraints can play decisive roles in translation, resulting in divergences and other issues. The primary objective of this paper is to understand the types of divergence problems that operate behind English to Bengali translation the study of which is still in a state of its infancy. Proper identification and understanding of these problems are important in both manual and machine translation. Moreover, resolution of these problems is a pre-requisite for designing a robust machine translation system between the languages considered for the present study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".