  Latent space based GAN methods and attention based sequence to sequence
models have achieved impressive results in text generation and unsupervised
machine translation respectively. Leveraging the two domains, we propose an
adversarial latent space based model capable of generating parallel sentences
in two languages concurrently and translating bidirectionally. The bilingual
generation goal is achieved by sampling from the latent space that is shared
between both languages. First two denoising autoencoders are trained, with
shared encoders and back-translation to enforce a shared latent state between
the two languages. The decoder is shared for the two translation directions.
Next, a GAN is trained to generate synthetic "code" mimicking the languages'
shared latent space. This code is then fed into the decoder to generate text in
either language. We perform our experiments on Europarl and Multi30k datasets,
on the English-French language pair, and document our performance using both
supervised and unsupervised machine translation.
