Accelerated Reinforcement Learning for Sentence Generationby Vocabulary Prediction
Table 2: BLEU scores for the development splits of the six datasets. “Small softmax” corresponds to our method.
['[EMPTY]', '[EMPTY]', 'Cross entropy Small softmax', 'Cross entropy Full softmax', 'REINFORCE w/ cross entropy Small softmax', 'REINFORCE w/ cross entropy Full softmax']
The goal of this paper is achieving efficient reinforcement learning for sentence generation to encourage future research, but before evaluating the efficiency of our method, we show that using the small vocabularies does not degrade the accuracy of the sentence generation models. The BLEU scores are averaged over five different runs with different random seeds, and the standard deviations are also reported. For some datasets, the BLEU scores of our method are even better than those of the full softmax method. The trend is consistent in both of the cross entropy training phase and the REINFORCE phase. These results indicate that our method works well for different machine translation and image captioning datasets. We also confirmed that our experimental results are competitive with previously reported results when using the same training datasets; for example, our En-Vi test set