Empirical Prior for Text Autoencoders

Abstract

This paper explores the application of Variational Autoencoders (VAE) in text generation, focusing on overcoming challenges like posterior collapse and the limitations of simplistic prior distributions. We investigate a transition from VAE to text autoencoders (AE), which model a compact latent space and preserves the capability of the language model itself. Our method involves layer-wise latent vectors regularized by orthogonal constraints to encourage distinct semantic spaces. In particular, we estimate an empirical prior online from the learned latent vectors to support sampling during generation like VAE. Experimental results on standard benchmarks demonstrate that the autoencoders generate higher quality and more diverse text than the VAE-based Transformer baselines, offering an effective alternative for generative language modeling.