A Surprisingly Effective Fix for Deep Latent Variable Modeling of Text
Table 8: Accuracy on Yelp of unsupervised and supervised classification. Evaluated via accuracy. #labeled denotes the number of labeled example during training.
['[BOLD] #labeled', '[BOLD] 0', '[BOLD] 100', '[BOLD] 500', '[BOLD] 1k', '[BOLD] 2k', '[BOLD] 10k']
To further evaluate the quality of the latent codes, we train a Gaussian mixture model (for unsupervised clustering) or a one-layer linear classifier (for supervised classification) on the pretrained latent codes. We work with a downsampled version of Yelp sentiment dataset collected by Shen et al. For FBP and our method, the target rate is selected in terms of the average validation accuracy. Our method consistently yields the best results on all settings – remarkably, its performance with only 100 labeled samples already surpasses others with 10k labels.