When and Why is Unsupervised Neural Machine Translation Useless?

Understand

This paper studies the practicality of the current state-of-the-art unsupervised methods in neural machine translation (NMT).

• In ten translation tasks with various data settings, we analyze the conditions under which the unsupervised methods fail to produce reasonable translations.
• We show that their performance is severely affected by linguistic dissimilarity and domain mismatch between source and target monolingual data.
• Such conditions are common for low-resource language pairs, where unsupervised learning works poorly.