  We investigate different approaches to translate between similar languages
under low resource conditions, as part of our contribution to the WMT 2020
Similar Languages Translation Shared Task. We submitted Transformer-based
bilingual and multilingual systems for all language pairs, in the two
directions. We also leverage back-translation for one of the language pairs,
acquiring an improvement of more than 3 BLEU points. We interpret our results
in light of the degree of mutual intelligibility (based on Jaccard similarity)
between each pair, finding a positive correlation between mutual
intelligibility and model performance. Our Spanish-Catalan model has the best
performance of all the five language pairs. Except for the case of
Hindi-Marathi, our bilingual models achieve better performance than the
multilingual models on all pairs.
