  This paper describes the submission of LMU Munich to the WMT 2020
unsupervised shared task, in two language directions, German<->Upper Sorbian.
Our core unsupervised neural machine translation (UNMT) system follows the
strategy of Chronopoulou et al. (2020), using a monolingual pretrained language
generation model (on German) and fine-tuning it on both German and Upper
Sorbian, before initializing a UNMT model, which is trained with online
backtranslation. Pseudo-parallel data obtained from an unsupervised statistical
machine translation (USMT) system is used to fine-tune the UNMT model. We also
apply BPE-Dropout to the low resource (Upper Sorbian) data to obtain a more
robust system. We additionally experiment with residual adapters and find them
useful in the Upper Sorbian->German direction. We explore sampling during
backtranslation and curriculum learning to use SMT translations in a more
principled way. Finally, we ensemble our best-performing systems and reach a
BLEU score of 32.4 on German->Upper Sorbian and 35.2 on Upper Sorbian->German.
