Incorporating Discrete Translation Lexicons into Neural Machine Translation
Table 2: Accuracies for the baseline attentional NMT (attn) and the proposed bias-based method using the automatic (auto-bias) or hybrid (hyb-bias) dictionaries. Bold indicates a gain over the attn baseline, † indicates a significant increase at p<0.05, and ∗ indicates p<0.10. Traditional phrase-based (pbmt) and hierarchical phrase based (hiero) systems are shown for reference.
['System', 'BTEC BLEU', 'BTEC NIST', 'BTEC RECALL', 'KFTT BLEU', 'KFTT NIST', 'KFTT RECALL']
In this section, we first a detailed examination of the utility of the proposed bias method when used with the auto or hyb lexicons, which empirically gave the best results, and perform a comparison among the other lexicon integration methods in the following section.