Task: Language Identification
Input (Spanish, English): The authors propose to sample from VAEs through a Markov chain [z_t ~ q(z|x=x_{t-1}), x_t ~ p(x|z=z_t)]. The paper uses confusing notation, oversells the novelty, ignoring some relevant previous results. The qualitative difference between regular sampling and this Gibbs chain is not very convincing, judging from the figures. It would be a great workshop paper (perhaps more), if the authors fix the notation, fix the discussion to related work, and produce more convincing (perhaps simply upscaled?) figures.  Comments:   - Rezende et al's (2014) original VAE paper already discusses the Markov chain, which is ignored in this paper  - Notation is nonstandard / confusing. At page 1, it’s unclear what the authors mean with “p(x|z) which is approximated as q(x|z)”. - It’s also not clear what’s meant with q(z). At page 2, q(z) is called the learned distribution, while p(z) can in general also be a learned distribution. - It’s not true that it’s impossible to draw samples from q(z): one can sample x ~ q(x) from the dataset, then draw z ~ q(z|x). - It's not explained whether the analysis only applies to continuous observed spaces, or also discrete observed spaces - Figures 3 and 4 are not very convincing.
Output (English): en