Domain Control for Neural Machine Translation
Table 2: BLEU scores for the different systems and RNN-based domain classifier accuracy.
['[BOLD] Domain Constraint', '[BOLD] Single None', '[BOLD] Join None', '[BOLD] Token Oracle', '[BOLD] Feature Oracle', '[BOLD] Feature RNN', '[BOLD] Feature Acc (%)']
As expected, the Join model outperforms all Single models on their corresponding test sets, showing that NMT engines benefit from additional training data. Differences in accuracy are lower for domains with a higher representation in the Join model, like Parliamentary and Tourism. No domain information is used on these first configurations (none). Finally, we also evaluate the ability of our presented approach (Feature) to face test sets for which the domain is not known in advance. Hence, before translation, the domain tag is automatically detected using an in-house domain classification module based on RNN technology to disambiguate between the six different domains. The tool predicts the domain on a sentence-by-sentence basis, then translation is carried out using the predicted domain value in Feature model.