The AIC System for the WMT 2022 Unsupervised MT and Very Low Resource Supervised MT Task

AbstractThis paper presents our submissions to WMT 22 shared task in the Unsupervised and Very Low Resource Supervised Machine Translation tasks. The task revolves around translating between German ↔ Upper Sorbian (de ↔ hsb), German ↔ Lower Sorbian (de ↔ dsb) and Upper Sorbian ↔ Lower Sorbian (hsb ↔ dsb) in both unsupervised and supervised manner. For the unsupervised system, we trained an unsupervised phrase-based statistical machine translation (UPBSMT) system on each pair independently. We pretrained a De-Salvic mBART model on the following languages Polish (pl), Czech (cs), German (de), Upper Sorbian (hsb), Lower Sorbian (dsb). We then fine-tuned our mBART on the synthetic parallel data generated by the (UPBSMT) model along with authentic parallel data (de ↔ pl, de ↔ cs). We further fine-tuned our unsupervised system on authentic parallel data (hsb ↔ dsb, de ↔ dsb, de ↔ hsb) to submit our supervised low-resource system.