Neural Machine Translation with Reconstruction
Table 3: Translation performances of different decoding beams on the validation set, in which the averaged length of reference translations is 27.0.
['[BOLD] Beam', '[BOLD] Likelihood BLEU', '[BOLD] Likelihood Length', '[BOLD] +Reconstruction BLEU', '[BOLD] +Reconstruction Length']
Can our approach cope with the limitation of likelihood in large decoding spaces? Our approach can indeed solve the problem: increasing the size of decoding space generally leads to improving the BLEU score. We attribute this to the ability of the combined objective to measure both fluency and adequacy of translation candidates. There is a significant gap between k=10 and k=100. However, keeping increasing k does not result in significant improvements of translation accuracy but greatly decreases decoding efficiency. Therefore, in the following experiments we set the max value of k to 100, and use normalized likelihood for k=100 if we don’t use reconstruction in testing.