  In this work, we propose a flow-adapter architecture for unsupervised NMT. It
leverages normalizing flows to explicitly model the distributions of
sentence-level latent representations, which are subsequently used in
conjunction with the attention mechanism for the translation task. The primary
novelties of our model are: (a) capturing language-specific sentence
representations separately for each language using normalizing flows and (b)
using a simple transformation of these latent representations for translating
from one language to another. This architecture allows for unsupervised
training of each language independently. While there is prior work on latent
variables for supervised MT, to the best of our knowledge, this is the first
work that uses latent variables and normalizing flows for unsupervised MT. We
obtain competitive results on several unsupervised MT benchmarks.
