Reordering: a stepping-stone to perfect Thai Sign generation
Bibliographic record
Abstract
We proposed the Sign Code Reordering approach (SCR) for reordering the intermediate sign codes (ISC) to Sign code script (SCS) generation. SCR uses language structure matching techniques to reduce complicated grammar rules, provide efficient results. SCR comprises three steps: extraction, reordering and integration. The distinction between source and target language in both grammar and vocabulary is concerned in each processing step to ensure the accuracy of reordering. SCR focuses on accurate and acceptable reordering that are not conforming to the original structure. SCR was designed to capture linguistic differences such as phrase, sentence and multi-sentence structures, no matter how long the input sentence is. The SCR prototype system was implemented and tested to reorder a number ISCs. The test results have been proved that SCR arranges ISCs successfully. SCR can be augmented into any NLP application which requires ISC arrangement e.g., T3STS. T3STS translates Thai text into Thai Sign language. Thai Sign language is the language of the Deaf in Thailand.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".