Most existing methods for automatic bilingual dictionary induction rely on
prior alignments between the source and target languages, such as parallel
corpora or seed dictionaries. For many language pairs, such supervised
alignments are not readily available. We propose an unsupervised approach for
learning a bilingual dictionary for a pair of languages given their
independently-learned monolingual word embeddings. The proposed method exploits
local and global structures in monolingual vector spaces to align them such
that similar words are mapped to each other. We show empirically that the
performance of bilingual correspondents learned using our proposed unsupervised
method is comparable to that of using supervised bilingual correspondents from
a seed dictionary.

本文提出了一种无监督的方法来学习一对语言的双语词典，并利用单语向量空间中的局部和全局结构将它们对齐，从而使相似的单词映射到一起，实验表明，使用提出的无监督方法学习的双语对应词性能与使用监督学习的双语对应词从种子词典中学习的相当。