Filtering and Extended Vocabulary based Translation for Low-resource Language Pair of Sanskrit-Hindi
Why this work is in the frame
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Bibliographic record
Abstract
Neural Machine Translation (NMT) is widely employed for language translation tasks because it performs better than the conventional statistical and phrase-based approaches. However, NMT techniques involve challenges, such as requiring a large and clean corpus of parallel data and the inability to deal with rare words. They need to be faster for real-time applications. More work needs to be done using NMT to address the challenges in translating Sanskrit, one of the oldest and rich languages known to the world, with its morphological richness and limited multilingual parallel corpus. There is usually no similar data between a language pair; hence, no application exists so far that can translate Sanskrit to/from other languages. This study presents an in-depth analysis to address these challenges with the help of a low-resource Sanskrit-Hindi language pair. We employ a novel training corpus filtering with extended vocabulary in a zero-shot transformer architecture. The structure of the Sanskrit language is thoroughly investigated to justify the use of each step. Furthermore, the proposed method is analyzed based on variations in sentence length and also applied to a high-resource language pair in order to demonstrate its efficacy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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 it