Preventing Posterior Collapse with Levenshtein Variational Autoencoder
- URL: http://arxiv.org/abs/2004.14758v1
- Date: Thu, 30 Apr 2020 13:27:26 GMT
- Title: Preventing Posterior Collapse with Levenshtein Variational Autoencoder
- Authors: Serhii Havrylov, Ivan Titov
- Abstract summary: We propose to replace the evidence lower bound (ELBO) with a new objective which is simple to optimize and prevents posterior collapse.
We show that Levenstein VAE produces more informative latent representations than alternative approaches to preventing posterior collapse.
- Score: 61.30283661804425
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Variational autoencoders (VAEs) are a standard framework for inducing latent
variable models that have been shown effective in learning text representations
as well as in text generation. The key challenge with using VAEs is the {\it
posterior collapse} problem: learning tends to converge to trivial solutions
where the generators ignore latent variables. In our Levenstein VAE, we propose
to replace the evidence lower bound (ELBO) with a new objective which is simple
to optimize and prevents posterior collapse. Intuitively, it corresponds to
generating a sequence from the autoencoder and encouraging the model to predict
an optimal continuation according to the Levenshtein distance (LD) with the
reference sentence at each time step in the generated sequence. We motivate the
method from the probabilistic perspective by showing that it is closely related
to optimizing a bound on the intractable Kullback-Leibler divergence of an
LD-based kernel density estimator from the model distribution. With this
objective, any generator disregarding latent variables will incur large
penalties and hence posterior collapse does not happen. We relate our approach
to policy distillation \cite{RossGB11} and dynamic oracles \cite{GoldbergN12}.
By considering Yelp and SNLI benchmarks, we show that Levenstein VAE produces
more informative latent representations than alternative approaches to
preventing posterior collapse.
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