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We propose a latent space energy-based prior model for text generation andclassification. The model stands on a generator network that generates the textsequence based on a continuous latent vector. The energy term of the priormodel couples a continuous latent vector and a symbolic one-hot vector, so thatdiscrete category can be inferred from the observed example based on thecontinuous latent vector. Such a latent space coupling naturally enablesincorporation of information bottleneck regularization to encourage thecontinuous latent vector to extract information from the observed example thatis informative of the underlying category. In our learning method, thesymbol-vector coupling, the generator network and the inference network arelearned jointly. Our model can be learned in an unsupervised setting where nocategory labels are provided. It can also be learned in semi-supervised settingwhere category labels are provided for a subset of training examples. Ourexperiments demonstrate that the proposed model learns well-structured andmeaningful latent space, which (1) guides the generator to generate text withhigh quality, diversity, and interpretability, and (2) effectively classifiestext.

Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification