Unsupervised Neural Translation Machine Translation
Artetxe, Cho
Traditionally NMT requires large parallel corpus. This method relies on unsupervised crosslingual embeddings. By a shared encoder for both translation encoder, the entire system is trained using monolingual data and reconstruct the input.
Keywords
crosslingual word embedding: TODO:next paper low resource neural machine translation: independently translation by a pivot language A>B>C to translate from A to C
Method
two layer encoder & two layer decoder: only one encoder shared by both languages
 dual structure: handle both directions together
 shared encoder: This universal encoder is aimed to produce a language independent representation of the input text, which each decoder should then transform into its corresponding language

fixed embedding: pretrained
decoder for its own language > reconstruct the other lanuage
shared encoder >into language independent embedding
(when inference, replace) decoder for the target language
denoising:
optimizes the probability of encoding a noised version of the sentence with shared encoder and reconstruct using L1 encoder. It exploit dual structure of machine translation. The shared encoder can reconstruct its own input.
backtranslation:
Iit translates the sentence in inference mode(encoding with shared & decoding with L2) and optimizes with probability of encoding the probability of translated sentence with shared encoder and recovering the original setence with L1 encoder.
Thoughts
This paper uses the method that we are familiar with, which reconstruct the input in unsupervised learning. It has two part that worthwhile considiering:
 pretrained embedding(which is key to my paper)
 a reconstruction that can translate from one to the other, in a totally unsupervised way.
If we can construct the embedding of time series in some way, and this way might offer a way to train a generative model that does the way for us.