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. In
all of our experiments, supervised and semi-supervised baselines with
50k-sentence bilingual data outperform the best unsupervised results. Our
analyses pinpoint the limits of the current unsupervised NMT and also suggest
immediate research directions.

本文研究了目前最先进的无监督神经机器翻译方法的实用性。我们分析了十个翻译任务的数据设置，研究了无监督方法无法生成合理翻译的条件，并表明这种情况对低资源语言对很常见，而在所有实验中，有监督和半监督基线都胜过了最好的无监督结果。我们的分析指出了目前无监督 NMT 的局限性，并提出了即刻的研究方向。