Zero-Shot Language Transfer vs Iterative Back Translation for Unsupervised Machine Translation
This work addresses translation challenges for low-resource languages, but it is incremental as it compares existing methods without introducing new techniques.
The paper compared zero-shot transfer learning and unsupervised machine translation for low-resource language pairs, examining how data size and domain affect performance, with code made available on GitHub.
This work focuses on comparing different solutions for machine translation on low resource language pairs, namely, with zero-shot transfer learning and unsupervised machine translation. We discuss how the data size affects the performance of both unsupervised MT and transfer learning. Additionally we also look at how the domain of the data affects the result of unsupervised MT. The code to all the experiments performed in this project are accessible on Github.