We describe the JD Explore Academy's submission of the WMT 2022 shared
general translation task. We participated in all high-resource tracks and one
medium-resource track, including Chinese-English, German-English,
Czech-English, Russian-English, and Japanese-English. We push the limit of our
previous work -- bidirectional training for translation by scaling up two main
factors, i.e. language pairs and model sizes, namely the \textbf{Vega-MT}
system. As for language pairs, we scale the "bidirectional" up to the
"multidirectional" settings, covering all participating languages, to exploit
the common knowledge across languages, and transfer them to the downstream
bilingual tasks. As for model sizes, we scale the Transformer-Big up to the
extremely large model that owns nearly 4.7 Billion parameters, to fully enhance
the model capacity for our Vega-MT. Also, we adopt the data augmentation
strategies, e.g. cycle translation for monolingual data, and bidirectional
self-training for bilingual and monolingual data, to comprehensively exploit
the bilingual and monolingual data. To adapt our Vega-MT to the general domain
test set, generalization tuning is designed. Based on the official automatic
scores of constrained systems, in terms of the sacreBLEU shown in Figure-1, we
got the 1st place on {Zh-En (33.5), En-Zh (49.7), De-En (33.7), En-De (37.8),
Cs-En (54.9), En-Cs (41.4) and En-Ru (32.7)}, 2nd place on {Ru-En (45.1) and
Ja-En (25.6)}, and 3rd place on {En-Ja(41.5)}, respectively; W.R.T the COMET,
we got the 1st place on {Zh-En (45.1), En-Zh (61.7), De-En (58.0), En-De
(63.2), Cs-En (74.7), Ru-En (64.9), En-Ru (69.6) and En-Ja (65.1)}, 2nd place
on {En-Cs (95.3) and Ja-En (40.6)}, respectively. Models will be released to
facilitate the MT community through GitHub and OmniForce Platform.

通过多向设置和数据增强等方式，我们的 Vega-MT 在多语种翻译任务中取得了第一名，并将模型释放到 GitHub 和 OmniForce 平台进行进一步使用。