Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation

Ruizhe Li, Xiao Li, Guanyi Chen, Chenghua Lin

Research output: Contribution to conferenceOral Presentation/ Invited Talk

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Abstract

The Variational Autoencoder (VAE) is a popular and powerful model applied to text modelling to generate diverse sentences. However, an issue known as posterior collapse (or KL loss vanishing) happens when the VAE is used in text modelling, where the approximate posterior collapses to the prior, and the model will totally ignore the latent variables and be degraded to a plain language model during text generation. Such an issue is particularly prevalent when RNN-based VAE models are employed for text modelling. In this paper, we propose a simple, generic architecture called Timestep-Wise Regularisation VAE (TWR-VAE), which can effectively avoid posterior collapse and can be applied to any RNN-based VAE models. The effectiveness and versatility of our model are demonstrated in different tasks, including language modelling and dialogue response generation.
Original languageEnglish
Publication statusPublished - 2 Nov 2020

Bibliographical note

Accepted by COLING 2020, final camera ready version

Keywords

  • cs.CL
  • cs.LG

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