Two Way Adversarial Unsupervised Word Translation
Computation and Language
2019-12-24 v1 Machine Learning
Machine Learning
Abstract
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.
Cite
@article{arxiv.1912.10168,
title = {Two Way Adversarial Unsupervised Word Translation},
author = {Blaine Cole},
journal= {arXiv preprint arXiv:1912.10168},
year = {2019}
}