HW - TSC ’s Participation in the WMT 2021 Triangular MT Shared Task (2021)

Abstract: This paper presents the submission of Huawei Translation Service Center (HW-TSC) to WMT 2021 Triangular MT Shared Task. We participate in the Russian-to-Chinese task under the constrained condition. We use Transformer architecture and obtain the best performance via a variant with larger parameter sizes. We perform detailed data pre-processing and filtering on the provided large-scale bilingual data. Several strategies are used to train our models, such as Multilingual Translation, Back Translation, Forward Translation, Data Denoising, Average Checkpoint, Ensemble, Fine-tuning, etc. Our system obtains 32.5 BLEU on the dev set and 27.7 BLEU on the test set, the highest score among all submissions.

## introduction this paper introduces our submission to the wmt21 triangular task. we adopt transformer @xcite architecture and strictly obey the constrained condition in terms of data usage. on one hand, we perform multiple data filtering strategies to enhance data quality; on the other hand, we leverage multilingual model @xcite , pivot language, forward @xcite and back translation @xcite , and data denoising @xcite strategies to further enhance training effects. in addition, we also adopt fine-tuning @xcite and ensemble @xcite , two widely used strategies, to further enhance system performance. we compare and contrast different strategies based on our experiment results and give our analysis accordingly. the overall training process is illustrated in figure 1 . section 2 mainly focuses on our training techniques, including model architecture, data processing and training strategies. section 3 describes our experiment settings and training process. section 4 presents the experiment results while section 5 analyze how our multilingual, data denoise and data augmentation strategies influence system performances. ## experiment result our overall training strategy is to train a baseline model, conduct incremental training with techniques such as multilingual model, denoise training, data augmentation, and fine-tuning. our submitted results come from ensembled models. ## conclusion this paper presents hw-tsc's submission to wmt21 triangular machine translation task. in general, we use transformer architecture and explore multiple data filtering and selection methods. in terms of training and data processing strategies, multilingual model, denoising training, data augmentation, and ftst we used can effectively improve system performance. our final result achieves an increase of 5.9 bleu when comparing baseline model on the dev set and gain a bleu score of 27.7 on the test which is the highest among all submissions.