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
this https URL

本报告介绍了我们的 VolcTrans 系统，它是一个基于 Transformer 的多语言模型，使用从多个来源收集的数据进行训练，包括来自数据轨道的公共训练集，由 Meta AI 提供的 NLLB 数据，自收集的平行语料库和来自反向翻译的伪双语文本。在官方测试集上，我们的系统实现了 17.3 BLEU，21.9 spBLEU，和 41.9 chrF2 ++，平均所有语言对的推理速度为每秒 11.5 句，使用单个 Nvidia Tesla V100 GPU。