In spite of the recent success of neural machine translation (NMT) in standard benchmarks, the lack of large parallel corpora poses a major practical problem for many language pairs. There have been several proposals to alleviate this issue with, for instance, triangulation and semi-supervised learning techniques, but they still require a strong cross-lingual signal. In this work, we completely remove the need of parallel data and propose a novel method to train an NMT system in a completely unsupervised manner, relying on nothing but monolingual corpora.
|Task||Dataset||Model||Metric name||Metric value||Global rank||Compare|
|Machine Translation||WMT2014 English-French||Unsupervised attentional encoder-decoder + BPE||BLEU score||14.36||# 31|
|Machine Translation||WMT2015 English-German||Unsupervised attentional encoder-decoder + BPE||BLEU score||6.89||# 6|