Unsupervised Neural Machine Translation

ICLR 2018 Mikel ArtetxeGorka LabakaEneko AgirreKyunghyun Cho

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... (read more)

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Evaluation results from the paper


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