State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent works showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed encouraging results, they are not on par with their supervised counterparts and are limited to pairs of languages sharing a common alphabet. In this work, we show that we can build a bilingual dictionary between two languages without using any parallel corpora, by aligning monolingual word embedding spaces in an unsupervised way. Without using any character information, our model even outperforms existing supervised methods on cross-lingual tasks for some language pairs. Our experiments demonstrate that our method works very well also for distant language pairs, like English-Russian or English-Chinese. We finally show that our method is a first step towards fully unsupervised machine translation and describe experiments on the English-Esperanto language pair, on which there only exists a limited amount of parallel data. Recent attempts at reducing the need for bilingual supervision (Smith et al., 2017) employ identical character strings to form a parallel vocabulary. The iterative method of Artetxe et al. (2017) gradually aligns embedding spaces, starting from a parallel vocabulary of aligned digits. These methods are however limited to similar languages sharing a common alphabet, such as European languages. Going further, Cao et al. (2016) perform moment matching and Zhang et al. (2017) adopt adversarial training to obtain cross-lingual word embeddings without parallel data. While these approaches sound appealing, their performance is significantly below supervised methods. To sum up, current * Equal contribution. Order has been determined with a coin flip.
REF leveraged adversarial training to learn a linear mapping from a source to a target space without using parallel data.
Word Translation Without Parallel Data
ArXiv
ArXiv
Computer Science