Unsupervised Neural Translation Machine Translation

Posted by Kaiyuan Chen on November 30, 2017

Unsupervised Neural Translation Machine Translation

Artetxe, Cho

Traditionally NMT requires large parallel corpus. This method relies on unsupervised cross-lingual embeddings. By a shared encoder for both translation encoder, the entire system is trained using monolingual data and reconstruct the input.


cross-lingual 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


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: pre-trained

              decoder for its own language -> reconstruct the other lanuage

shared encoder ->into language independent embedding

            (when inference, replace) decoder for the target language


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.


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.


This paper uses the method that we are familiar with, which reconstruct the input in unsupervised learning. It has two part that worthwhile considiering:

  • pre-trained 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.