Cross-domain Generalization Performance of a Neural Machine Translation System Incorporating Hierarchical Bayesian Models
Bibliographic record
Abstract
The study combines hierarchical Bayesian model and adversarial neural network according to the model architecture of neural machine translation, and introduces the domain generalization method based on cross-domain gating to solve the domain generalization problem, and constructs the neural machine translation system based on hierarchical Bayesian model.Translation performance experiments are conducted on this translation system to test the cross-domain generalization performance of the neural machine translation system based on hierarchical Bayesian model in this paper.The translation method of this paper significantly outperforms the baseline system of statistical machine translation in the direction of translation for all the intertranslated languages and medial languages of the European Parliament corpus.The statistical machine translation model and the standard neural machine translation model have maintained a stable performance during the growth of the interpolation coefficients, while the performance of this paper's hierarchical Bayesian-based neural machine translation system grows rapidly to the maximum when the interpolation coefficients grow to 0.3 or 0.4, and its overall average BLEU value always outperforms that of the statistical machine translation model and the standard neural machine translation model.The BLEU values of the hierarchical Bayesian-based neural machine translation system are 35.26%and 34.28% for bidirectional Chinese-English translation, and 26.42% and 25.96% for bi-directional Chinese-Western translation, which are better than those of the neural machine translation based on the attentional mechanism and variational scoring.And the hierarchical Bayesian-based neural machine translation system has strong stability on the translation of low-resource languages.
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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 | 0.003 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".