Tencent’s Multilingual Machine Translation System for WMT 22 Large-Scale A frican Languages (2022)

Abstract: This paper describes Tencent’s multilingual machine translation systems for the WMT22 shared task on Large-Scale Machine Translation Evaluation for African Languages. We participated in the constrained translation track in which only the data and pretrained models provided by the organizer are allowed. The task is challenging due to three problems, including the absence of training data for some to-be-evaluated language pairs, the uneven optimization of language pairs caused by data imbalance, and the curse of multilinguality. To address these problems, we adopt data augmentation, distributionally robust optimization, and language family grouping, respectively, to develop our multilingual neural machine translation (MNMT) models. Our submissions won the 1st place on the blind test sets in terms of the automatic evaluation metrics. Codes, models, and detailed competition results are available at https://github.com/wxjiao/WMT2022-Large-Scale-African .

## introduction multilingual neural machine translation (mnmt) aims to translate between multiple language pairs with a unified model @xcite . it is appealing due to the model efficiency, easy deployment, and knowledge transfer between high resource languages and low resource languages. hence, mnmt has attracted more and more attention from both academia and industry. to improve the performance of mnmt models, previous researchers have proposed various approaches on advanced model architectures @xcite , training strategies @xcite , and data utilization @xcite . in addition, industrial companies have released massive multilingual pretrained models @xcite and large-scale multilingual translation models @xcite to facilitate translation among hundreds of languages. however, existing efforts on mnmt for african languages are not sufficient due to the lack of high quality and standardized evaluation benchmarks. in this paper, we build a system integrating several advanced approaches for wmt22 large-scale machine translation evaluation task @xcite , which involves a set of 24 african languages. we participated in the constrained translation track, where only the data provided by the organizer are allowed. this task is challenging due to three potential problems: experimental results show that our system can significantly improve the performance of vanilla mnmt models, from 15.50 to 17.95 bleu points ( §4.2). extensive analysis suggests that data augmentation could be harmful to the translation performance if used for training the final models ## data in this section, we present the details of our data preparation. ## conclusion in this paper, we describe tencent's multilingual machine translation systems for the wmt22 shared task on large-scale machine translation evaluation for african languages. we address three key challenges of this task by data augmentation, distributionally robust optimization (dro), and language family grouping, respectively, to develop our mnmt models. our submissions won the 1st place in the constrained track. extensive analyses also point out the drawbacks of larger models and dro in addressing the curse of multilinguality, which warrants further research in the future.