Toward Controlled Generation of Text
Table 1: Sentiment accuracy of generated sentences. S-VAE (Kingma et al., 2014) and our model are trained on the three sentiment datasets and generate 30K sentences, respectively.
['Model', 'Dataset SST-full', 'Dataset SST-small', 'Dataset Lexicon']
Specifically, we generate sentences given sentiment code c, and use the pre-trained sentiment classifier to assign sentiment labels to the generated sentences. We see that our method consistently outperforms S-VAE on all datasets. In particular, trained with only 250 labeled examples in SST-small, our model achieves reasonable generation accuracy, demonstrating the ability of learning disentangled representations with very little supervision. More importantly, given only word-level annotations in Lexicon, our model successfully transfers the knowledge to sentence level and generates desired sentiments reasonably well. Compared to our method that drives learning by directly assessing generated sentences, S-VAE attempts to capture sentiment semantics only by reconstructing labeled words, which is less efficient and gives inferior performance.