On Variational Learning of Controllable Representations for Text without Supervision

Peng Xu, Jackie Chi Kit Cheung, Yanshuai Cao
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10534-10543, 2020.

Abstract

The variational autoencoder (VAE) can learn the manifold of natural images on certain datasets, as evidenced by meaningful interpolating or extrapolating in the continuous latent space. However, on discrete data such as text, it is unclear if unsupervised learning can discover similar latent space that allows controllable manipulation. In this work, we find that sequence VAEs trained on text fail to properly decode when the latent codes are manipulated, because the modified codes often land in holes or vacant regions in the aggregated posterior latent space, where the decoding network fails to generalize. Both as a validation of the explanation and as a fix to the problem, we propose to constrain the posterior mean to a learned probability simplex, and performs manipulation within this simplex. Our proposed method mitigates the latent vacancy problem and achieves the first success in unsupervised learning of controllable representations for text. Empirically, our method outperforms unsupervised baselines and strong supervised approaches on text style transfer, and is capable of performing more flexible fine-grained control over text generation than existing methods.

Cite this Paper


BibTeX
@InProceedings{pmlr-v119-xu20a, title = {On Variational Learning of Controllable Representations for Text without Supervision}, author = {Xu, Peng and Cheung, Jackie Chi Kit and Cao, Yanshuai}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {10534--10543}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/xu20a/xu20a.pdf}, url = {http://proceedings.mlr.press/v119/xu20a.html}, abstract = {The variational autoencoder (VAE) can learn the manifold of natural images on certain datasets, as evidenced by meaningful interpolating or extrapolating in the continuous latent space. However, on discrete data such as text, it is unclear if unsupervised learning can discover similar latent space that allows controllable manipulation. In this work, we find that sequence VAEs trained on text fail to properly decode when the latent codes are manipulated, because the modified codes often land in holes or vacant regions in the aggregated posterior latent space, where the decoding network fails to generalize. Both as a validation of the explanation and as a fix to the problem, we propose to constrain the posterior mean to a learned probability simplex, and performs manipulation within this simplex. Our proposed method mitigates the latent vacancy problem and achieves the first success in unsupervised learning of controllable representations for text. Empirically, our method outperforms unsupervised baselines and strong supervised approaches on text style transfer, and is capable of performing more flexible fine-grained control over text generation than existing methods.} }
Endnote
%0 Conference Paper %T On Variational Learning of Controllable Representations for Text without Supervision %A Peng Xu %A Jackie Chi Kit Cheung %A Yanshuai Cao %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-xu20a %I PMLR %P 10534--10543 %U http://proceedings.mlr.press/v119/xu20a.html %V 119 %X The variational autoencoder (VAE) can learn the manifold of natural images on certain datasets, as evidenced by meaningful interpolating or extrapolating in the continuous latent space. However, on discrete data such as text, it is unclear if unsupervised learning can discover similar latent space that allows controllable manipulation. In this work, we find that sequence VAEs trained on text fail to properly decode when the latent codes are manipulated, because the modified codes often land in holes or vacant regions in the aggregated posterior latent space, where the decoding network fails to generalize. Both as a validation of the explanation and as a fix to the problem, we propose to constrain the posterior mean to a learned probability simplex, and performs manipulation within this simplex. Our proposed method mitigates the latent vacancy problem and achieves the first success in unsupervised learning of controllable representations for text. Empirically, our method outperforms unsupervised baselines and strong supervised approaches on text style transfer, and is capable of performing more flexible fine-grained control over text generation than existing methods.
APA
Xu, P., Cheung, J.C.K. & Cao, Y.. (2020). On Variational Learning of Controllable Representations for Text without Supervision. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:10534-10543 Available from http://proceedings.mlr.press/v119/xu20a.html.

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