Lexical Based Reordering Models for English to Telugu Machine Translation
Bibliographic record
Abstract
Telugu is one of the commonly spoken regional language in India.It is mostly spoken among the states of Andhra Pradesh and Telangana.In rural areas it is difficult for the people to understand the non-regional language specially while at the time of government works, land dealing transactions etc. Due to this there is a scope to develop a machine translation model from English to Telugu.The machine translation is an automatic technique of translating one language to another through Language Processing approach.To understand the Telugu language translation, the structural comparisons are done among English and Telugu languages to attain standard outcome.In this work, Lexical based reordering statistical model (LBRSM) is used for language conversion.This analyzes the language structure outcomes between word, phrase and hierarchical based models for the translation quality purpose.To maintain good quality translation TER and BLEU metrics 62.01 and 29.07 are considered for finding n-gram exact matches.From this work, the Phrase based reordering statistical model (PBRSM) achieved better results when compared with other system models in both training and testing phases.
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 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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".