Advances and challenges in unsupervised neural machine translation

1Citations
Citations of this article
63Readers
Mendeley users who have this article in their library.

Abstract

Unsupervised cross-lingual language representation initialization methods, together with mechanisms such as denoising and back-translation, have advanced unsupervised neural machine translation (UNMT), which has achieved impressive results. Meanwhile, there are still several challenges for UNMT. This tutorial first introduces the background and the latest progress of UNMT. We then examine a number of challenges to UNMT and give empirical results on how well the technology currently holds up1

Cite

CITATION STYLE

APA

Wang, R., & Zhao, H. (2021). Advances and challenges in unsupervised neural machine translation. In EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Tutorial Abstracts (pp. 17–21). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.eacl-tutorials.5

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free