Neural Machine Translation Decoding with Terminology Constraints
Table 3: Bleu scores and speed ratios relative to unconstrained Lnmt for production system with up to c constraints per sentence (newstest2017). A: secondary attention, B, C: allow 1 or 2 extra tokens, respectively (Section 2.3). Dict (v2∗) refers to decoding with attentions but without A, B or C.
['[ITALIC] eng-ger-wmt17', 'Bleu [BOLD] /speed ratio  [ITALIC] c=2', 'Bleu [BOLD] /speed ratio  [ITALIC] c=2', 'Bleu [BOLD] /speed ratio  [ITALIC] c=3', 'Bleu [BOLD] /speed ratio  [ITALIC] c=3', 'Bleu [BOLD] /speed ratio  [ITALIC] c=4', 'Bleu [BOLD] /speed ratio  [ITALIC] c=4']
Tab. Rows two and three confirm that the reduced computational complexity of our approach yields faster decoding speeds than the approach of Anderson et al. while incurring a small decrease in Bleu. Moreover, it compares favourably for larger numbers of constraints per sentence: v2 * is 3.5x faster than v1 for c=2 and more than 5x faster for c=4. Relaxing the restrictions of decoding with attentions improves the Bleu scores but increases runtime. However, the slowest v2 configuration is still faster than v1. The optimal trade-off between quality and speed is likely to differ for each language pair.