An Integrated Deep Generative Model for Text Classification and Generation

Abstract

Text classification and generation are two important tasks in the field of natural language processing. In this paper, we deal with both tasks via Variational Autoencoder, which is a powerful deep generative model. The self-attention mechanism is introduced to the encoder. The modified encoder extracts the global feature of the input text to produce the hidden code, and we train a neural network classifier based on the hidden code to perform the classification. On the other hand, the label of the text is fed into the decoder explicitly to enhance the categorization information, which could help with text generation. The experiments have shown that our model could achieve competitive classification results and the generated text is realistic. Thus the proposed integrated deep generative model could be an alternative for both tasks.

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“…5): autoregressive generative models (ARM), flow-based models, and latent variable models. Text analysis [30], image analysis [29], audio analysis [31], active learning [32], reinforcement learning [33], graph analysis [34], medical imaging [35], image compression [36], and other applications use deep generative modeling. Unsupervised learning is a subfield of machine learning that contains numerous algorithms with varying objectives.…”

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“…Although EDA reduces overfitting when training on smaller datasets, the improvement is at times marginal. Wang and Wu [25] proposed a framework that combines variational autoencoder (VAE) and neural networks to deal with text classification and generation tasks. GAN [10,26] was firstly proposed for continuous data (image generation, inpainting, style transfer, etc.)…”

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“…The experimental results demonstrate that the classification model based on deep learning shows excellent performance compared to the kernel functions [3]. With the proposal of CNN classification model, more and more deep neural network models [4]- [8] have achieved gratifying results in the task of text classification.…”