Two Way Adversarial Unsupervised Word Translation
This addresses the challenge of building word-level translation models without supervision, which is incremental as it builds on existing unsupervised approaches.
The paper tackles the problem of unsupervised word translation by proposing a method that jointly learns translations in both directions between a pair of languages, resulting in improved accuracy over past methods.
Word translation is a problem in machine translation that seeks to build models that recover word level correspondence between languages. Recent approaches to this problem have shown that word translation models can learned with very small seeding dictionaries, and even without any starting supervision. In this paper we propose a method to jointly find translations between a pair of languages. Not only does our method learn translations in both directions but it improves accuracy of those translations over past methods.