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

SJTU-NICT's Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task