Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improved Variational Neural Machine Translation by Promoting Mutual
  Information   Posterior collapse plagues VAEs for text, especially for conditional text
generation with strong autoregressive decoders. In this work, we address this
problem in variational neural machine translation by explicitly promoting
mutual information between the latent variables and the data. Our model extends
the conditional variational autoencoder (CVAE) with two new ingredients: first,
we propose a modified evidence lower bound (ELBO) objective which explicitly
promotes mutual information; second, we regularize the probabilities of the
decoder by mixing an auxiliary factorized distribution which is directly
predicted by the latent variables. We present empirical results on the
Transformer architecture and show the proposed model effectively addressed
posterior collapse: latent variables are no longer ignored in the presence of
powerful decoder. As a result, the proposed model yields improved translation
quality while demonstrating superior performance in terms of data efficiency
and robustness.

4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg