Variational Neural Machine Translation
Table 1: BLEU scores on the NIST Chinese-English translation task. AVG = average BLEU scores on test sets. We highlight the best results in bold for each test set. “↑/⇑”: significantly better than Moses (p < 0.05/p < 0.01); “+/++”: significantly better than GroundHog (p < 0.05/p < 0.01);
['[BOLD] System', '[BOLD] MT05', '[BOLD] MT02', '[BOLD] MT03', '[BOLD] MT04', '[BOLD] MT06', '[BOLD] MT08', '[BOLD] AVG']
Clearly VNMT significantly improves translation quality in terms of BLEU on most cases, and obtains the best average results that gain 0.86 and 1.35 BLEU points over Moses and GroundHog respectively. Besides, without the KL objective, VNMT w/o KL obtains even worse results than GroundHog. These results indicate the following two points: 1) explicitly modeling underlying semantics by a latent variable indeed benefits neural machine translation, and 2) the improvements of our model are not from enlarging the network.