Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation

Jul 12, 2020

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Interpretability is important in text generation for guiding the generation with interpretable attributes. Variational auto-encoder (VAE) with Gaussian distribution as prior has been successfully applied in text generation, but it is hard to interpret the meaning of the latent variable. To enhance the controllability and interpretability, one can replace the Gaussian prior with a mixture of Gaussian distributions (GM-VAE), whose mixture components could be related to some latent attributes of data. Unfortunately, straightforward variational training of GM-VAE leads the mode-collapse problem. In this paper, we find that mode-collapse is a general problem for VAEs with exponential family mixture priors. We propose DEM-VAE, which introduces an extra dispersion term to induce a well-structured latent space. Experimental results show that our approach does obtain a well structured latent space, with which our method outperforms strong baselines in interpretable text generation benchmarks.

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About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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