Neural Machine Translation with Explicit Phrase Alignment
Table 4: Results on the webpage translation task. “w/o tag” denotes the BLEU score without considering HTML tags, “w/ tag” denotes the BLEU score considering HTML tags, and “in tag” denotes the BLEU score considering only the text enclosed by tags. Note that Remove does not have the “in tag” BLEU score because all tags are removed.
['Method', 'w/o tag', 'w/ tag', 'in tag']
As Remove ignores all HTML tags, it is not capable of imposing structural constraints. Split ensures that the structural constraints can be imposed correctly because the sentence segments between HTML tags are translated independently, but the translation quality drops dramatically. Match and Align take the full advantage of standard NMT to translate the textual parts but often fail to recover HTML tags correctly after decoding. According to the translations, it seems that Google uses a strategy similar to Split but achieves much higher BLEU scores because it used much larger training data than all other methods. Our approach achieves the best performance in terms of all evaluation metrics by fully preserving the structural constraints without losing translation quality.