Bibliographic record
Abstract
Statistical machine translation systems are usually trained on large amounts of bilingual text and of monolingual text in the target language. In this paper, we will present a self-training approach, which additionally explores the use of monolingual source text, namely the documents to be translated, to improve the system performance. An initial version of the translation system is used to translate the source text. Among the generated translations, target sentences of low quality are automatically identified and discarded. The reliable translations together with their sources are then used as a new bilingual corpus for training an additional phrase translation model. Thus, the translation system can be adapted to the new source data even if no bilingual data in this domain is available. Experimental evaluation was performed on a standard ChineseEnglish translation task. We focus on settings where the domain and/or the style of the test data is different from that of the training material. We will show a significant improvement in translation quality through the use of the adaptive phrase translation model. BLEU score rises up to 1.1 points, and mWER is reduced by up to 3.1% absolute.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.014 |
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".