  This paper illustrates our approach to the shared task on large-scale
multilingual machine translation in the sixth conference on machine translation
(WMT-21). This work aims to build a single multilingual translation system with
a hypothesis that a universal cross-language representation leads to better
multilingual translation performance. We extend the exploration of different
back-translation methods from bilingual translation to multilingual
translation. Better performance is obtained by the constrained sampling method,
which is different from the finding of the bilingual translation. Besides, we
also explore the effect of vocabularies and the amount of synthetic data.
Surprisingly, the smaller size of vocabularies perform better, and the
extensive monolingual English data offers a modest improvement. We submitted to
both the small tasks and achieved the second place.
