Unsupervised machine translation---i.e., not assuming any cross-lingual
supervision signal, whether a dictionary, translations, or comparable
corpora---seems impossible, but nevertheless, Lample et al. (2018) recently
proposed a fully unsupervised machine translation (MT) model. The model relies
heavily on an adversarial, unsupervised alignment of word embedding spaces for
bilingual dictionary induction (Conneau et al., 2018), which we examine here.
Our results identify the limitations of current unsupervised MT: unsupervised
bilingual dictionary induction performs much worse on morphologically rich
languages that are not dependent marking, when monolingual corpora from
different domains or different embedding algorithms are used. We show that a
simple trick, exploiting a weak supervision signal from identical words,
enables more robust induction, and establish a near-perfect correlation between
unsupervised bilingual dictionary induction performance and a previously
unexplored graph similarity metric.

该文研究了无监督的机器翻译模型，提出了利用对手生成网络对词嵌入空间进行自对齐和对齐生成双语词典，但对于形态丰富的语言，无监督双语词典的表现会受到很大限制，该研究通过利用弱监督从同一词来构建词典，建立了无监督双语词典表现和图相似度的相关性。