  This paper describes Charles University submission for Multilingual
Low-Resource Translation for Indo-European Languages shared task at WMT21. We
competed in translation from Catalan into Romanian, Italian and Occitan. Our
systems are based on shared multilingual model. We show that using joint model
for multiple similar language pairs improves upon translation quality in each
pair. We also demonstrate that chararacter-level bilingual models are
competitive for very similar language pairs (Catalan-Occitan) but less so for
more distant pairs. We also describe our experiments with multi-task learning,
where aside from a textual translation, the models are also trained to perform
grapheme-to-phoneme conversion.
