HW-TSC’s Submissions to the WMT 2022 General Machine Translation Shared Task

AbstractThis paper presents the submissions of Huawei Translate Services Center (HW-TSC) to the WMT 2022 General Machine Translation Shared Task. We participate in 6 language pairs, including Zh↔En, Ru↔En, Uk↔En, Hr↔En, Uk↔Cs and Liv↔En. We use Transformer architecture and obtain the best performance via multiple variants with larger parameter sizes. We perform fine-grained pre-processing and filtering on the provided large-scale bilingual and monolingual datasets. For medium and highresource languages, we mainly use data augmentation strategies, including Back Translation, Self Training, Ensemble Knowledge Distillation, Multilingual, etc. For low-resource languages such as Liv, we use pre-trained machine translation models, and then continue training with Regularization Dropout (R-Drop). The previous mentioned data augmentation methods are also used. Our submissions obtain competitive results in the final evaluation.