  This report describes our VolcTrans system for the WMT22 shared task on
large-scale multilingual machine translation. We participated in the
unconstrained track which allows the use of external resources. Our system is a
transformerbased multilingual model trained on data from multiple sources
including the public training set from the data track, NLLB data provided by
Meta AI, self-collected parallel corpora, and pseudo bitext from
back-translation. A series of heuristic rules clean both bilingual and
monolingual texts. On the official test set, our system achieves 17.3 BLEU,
21.9 spBLEU, and 41.9 chrF2++ on average over all language pairs. The average
inference speed is 11.5 sentences per second using a single Nvidia Tesla V100
GPU. Our code and trained models are available at
https://github.com/xian8/wmt22
