Evaluation of Direct Machine Translation System For Punjabi To Hindi
Bibliographic record
Abstract
The Direct MT system is based upon exploitation of syntactic similarities between more or less related natural languages. Both Punjabi and Hindi languages have originated from Sanskrit which is one of the oldest language. In terms of speakers, Hindi is third most widely spoken language and Punjabi is twelfth most widely spoken language. Punjabi language is mostly used in the Northern India and in some areas of Pakistan as well as in UK, Canada and USA. Hindi is the national language of India and is spoken and used by the people all over the country. Hindi and Punjabi are closely related languages with lots of similarities in syntax and vocabulary. In the present study, a Punjabi to Hindi machine translation system using Direct MT approach has been developed and its output is evaluated by already prescribed methods in order to get the suitability of the system. It was observed that a fairly high accuracy Punjabi to Hindi Machine Translation System has been developed by direct word-for-word translation. The major inaccuracies in the direct translation are due to poor word choice for ambiguous words and some corrections regarding post positions in Hindi. 1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".