Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation

Ruizhe Li, Xiao Li, Guanyi Chen, Chenghua Lin


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.
Anthology ID:
2020.coling-main.216
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2381–2397
Language:
URL:
https://www.aclweb.org/anthology/2020.coling-main.216
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.coling-main.216.pdf