Cross-domain Generalization Performance of a Neural Machine Translation System Incorporating Hierarchical Bayesian Models

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.