Accelerated Reinforcement Learning for Sentence Generationby Vocabulary Prediction
Table 3: BLEU scores for the development split of the En-Ja (2M) and En-Ja (2M, SW) datasets.
['REINFORCE w/ cross entropy ( [ITALIC] K=1000)', '40.16']
These results show that our method consistently works even on these larger datasets at the word and subword levels. In this table we also report how our method works with beam search, and the greedy-based BLEU scores are very close to those of beam search after the REINFORCE phase. When performing a beam search, we can optionally use different sizes of the small vocabulary, but we observe that our method is robust to the changes, whereas Wu et al. We cite BLEU scores from previously published papers which reported results of single models (i.e., without ensemble). Our method with greedy translation achieves a competitive score. It should be noted that Morishita et al.