# CUNI Systems for the Unsupervised and Very Low Resource Translation Task in WMT20

arXiv: https://arxiv.org/abs/2010.11747 · HTML: https://mlsanity.com/paper/2010.11747/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [Small Batch Sizes Improve Training of Low-Resource Neural MT](https://mlsanity.com/paper/2203.10579/) (2022)

## Abstract (arXiv)

This paper presents a description of CUNI systems submitted to the WMT20 task on unsupervised and very low-resource supervised machine translation between German and Upper Sorbian. We experimented with training on synthetic data and pre-training on a related language pair. In the fully unsupervised scenario, we achieved 25.5 and 23.7 BLEU translating from and into Upper Sorbian, respectively. Our low-resource systems relied on transfer learning from German-Czech parallel data and achieved 57.4 BLEU and 56.1 BLEU, which is an improvement of 10 BLEU points over the baseline trained only on the available small German-Upper Sorbian parallel corpus.