  For most language combinations, parallel data is either scarce or simply
unavailable. To address this, unsupervised machine translation (UMT) exploits
large amounts of monolingual data by using synthetic data generation techniques
such as back-translation and noising, while self-supervised NMT (SSNMT)
identifies parallel sentences in smaller comparable data and trains on them. To
date, the inclusion of UMT data generation techniques in SSNMT has not been
investigated. We show that including UMT techniques into SSNMT significantly
outperforms SSNMT and UMT on all tested language pairs, with improvements of up
to +4.3 BLEU, +50.8 BLEU, +51.5 over SSNMT, statistical UMT and hybrid UMT,
respectively, on Afrikaans to English. We further show that the combination of
multilingual denoising autoencoding, SSNMT with backtranslation and bilingual
finetuning enables us to learn machine translation even for distant language
pairs for which only small amounts of monolingual data are available, e.g.
yielding BLEU scores of 11.6 (English to Swahili).
