A recent research line has obtained strong results on bilingual lexicon
induction by aligning independently trained word embeddings in two languages
and using the resulting cross-lingual embeddings to induce word translation
pairs through nearest neighbor or related retrieval methods. In this paper, we
propose an alternative approach to this problem that builds on the recent work
on unsupervised machine translation. This way, instead of directly inducing a
bilingual lexicon from cross-lingual embeddings, we use them to build a
phrase-table, combine it with a language model, and use the resulting machine
translation system to generate a synthetic parallel corpus, from which we
extract the bilingual lexicon using statistical word alignment techniques. As
such, our method can work with any word embedding and cross-lingual mapping
technique, and it does not require any additional resource besides the
monolingual corpus used to train the embeddings. When evaluated on the exact
same cross-lingual embeddings, our proposed method obtains an average
improvement of 6 accuracy points over nearest neighbor and 4 points over CSLS
retrieval, establishing a new state-of-the-art in the standard MUSE dataset.

本文提出一种新的方法，使用无监督机器翻译的方法生成合成平行语料库，进而提取双语词汇表。该方法可与任何词向量和跨语言映射技术一起使用，并且除了用于训练词向量的单语语料库外，不需要任何其他资源。在评估方面，与最近邻和 CSLS 技术相比，该方法在标准 MUSE 数据集上提高了 6 个准确度点，确立了新的最先进技术。