  Latent variable modeling in non-autoregressive neural machine translation
(NAT) is a promising approach to mitigate the multimodality problem. In the
previous works, they added an auxiliary model to estimate the posterior
distribution of the latent variable conditioned on the source and target
sentences. However, it causes several disadvantages, such as redundant
information extraction in the latent variable, increasing parameters, and a
tendency to ignore a part of the information from the inputs. In this paper, we
propose a new latent variable modeling that is based on a dual reconstruction
perspective and an advanced hierarchical latent modeling approach. Our proposed
method, {\em LadderNMT}, shares a latent space across both languages so that it
hypothetically alleviates or solves the above disadvantages. Experimental
results quantitatively and qualitatively demonstrate that our proposed latent
variable modeling learns an advantageous latent space and significantly
improves translation quality in WMT translation tasks.
