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Disentangling the Latent Space of (Variational) Autoencoders for NLP

  • Gino Brunner
  • Yuyi Wang
  • Roger Wattenhofer
  • Michael Weigelt
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 840)

Abstract

We train multi-task (variational) autoencoders on linguistic tasks and analyze the learned hidden sentence representations. The representations change significantly when translation and part-of-speech decoders are added. The more decoders are attached, the better the models cluster sentences according to their syntactic similarity, as the representation space becomes less entangled. We compare standard unconstrained autoencoders to variational autoencoders and find significant differences. We achieve better disentanglement with the standard autoencoder, which goes against recent work on variational autoencoders in the visual domain.

Keywords

NLP Variational Autoencoder Disentanglement Representation learning Syntax 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gino Brunner
    • 1
  • Yuyi Wang
    • 1
  • Roger Wattenhofer
    • 1
  • Michael Weigelt
    • 1
  1. 1.ETH ZurichZürichSwitzerland

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