Unsupervised Neural Dialect Translation with Commonality and Diversity Modeling
Table 2: Experimental results on unsupervised dialect neural machine translation. # Params (M): number of parameters in million. We can see that layer coordination provides improvement over baseline on both directions, and pivot-private embedding improves the result further by almost 10 BLEU scores on Can⇒Man. Combining both layer coordination and pivot-private embedding gives the best result, exceeding 12 and 5 BLEU scores than baseline NMT system on two directions, respectively.
['Model', 'Can⇒Man', 'Man⇒Can', '# Params (M)']
Tab. As seen, character-level rule-based translation system performs comparably with conventional unsupervised NMT system. This is in accord with \citeauthorlample2018phrase \shortcitelample2018phrase that training process of unsupervised NMT is vulnerable, because no aligned information between languages can be afforded to model training. Relatively, character transition rules offer adequate aligned references to conduct the fairish results. Besides, the unsupervised PB-SMT model performs slightly worse than NMT system, a possible reason is that the model is hard to extract a well-performed phrase table from colloquial data The model underperforms unsupervised NMT baseline, indicating that, to some extent, style transfer is not adequate for dialect translation.