A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings (ACL 2018)


Artetxe et al. propose a new unsupervised method for learning cross-lingual embeddings that builds on the self-learning method of previous work. The main insights are that self-learning requires an initialization that is better than random and that the distribution of similarity values of words that are translations of each other is similar. We can leverage the latter as initialization for unsupervised self-learning.


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