Evaluating Computational Language Models with Scaling Properties of Natural Language
Table 7: BLEU scores and perplexity for eval-AWD-LSTM-based evaluation on texts generated from the COCO image dataset by different GAN models: SeqGAN Yu et al. (2017), MaliGAN Che et al. (2017), RankGAN Lin et al. (2017), LeakGAN Guo et al. (2018), and TextGAN Zhang et al. (2017).
['[EMPTY]', 'SeqGAN', 'MaliGAN', 'RankGAN', 'LeakGAN', 'TextGAN', 'MLE', 'ImageCoco']
Nevertheless, other metrics would not provide a better evaluation in this case. The perplexity scores were computed by using the eval-AWD-LSTM model trained with the COCO image dataset and the hyperparameters for the PTB dataset. The perplexity of AWD-LSTM when trained with that dataset was 65.41.