  In this paper, we introduced our joint team SJTU-NICT 's participation in the
WMT 2020 machine translation shared task. In this shared task, we participated
in four translation directions of three language pairs: English-Chinese,
English-Polish on supervised machine translation track, German-Upper Sorbian on
low-resource and unsupervised machine translation tracks. Based on different
conditions of language pairs, we have experimented with diverse neural machine
translation (NMT) techniques: document-enhanced NMT, XLM pre-trained language
model enhanced NMT, bidirectional translation as a pre-training, reference
language based UNMT, data-dependent gaussian prior objective, and BT-BLEU
collaborative filtering self-training. We also used the TF-IDF algorithm to
filter the training set to obtain a domain more similar set with the test set
for finetuning. In our submissions, the primary systems won the first place on
English to Chinese, Polish to English, and German to Upper Sorbian translation
directions.
