Papago’s Submissions to the WMT 21 Triangular Translation Task (2021)

Abstract: This paper describes Naver Papago’s submission to the WMT21 shared triangular MT task to enhance the non-English MT system with tri-language parallel data. The provided parallel data are Russian-Chinese (direct), Russian-English (indirect), and English-Chinese (indirect) data. This task aims to improve the quality of the Russian-to-Chinese MT system by exploiting the direct and indirect parallel re- sources. The direct parallel data is noisy data crawled from the web. To alleviate the issue, we conduct extensive experiments to find effective data filtering methods. With the empirical knowledge that the performance of bilingual MT is better than multi-lingual MT and related experiment results, we approach this task as bilingual MT, where the two indirect data are transformed to direct data. In addition, we use the Transformer, a robust translation model, as our baseline and integrate several techniques, averaging checkpoints, model ensemble, and re-ranking. Our final system provides a 12.7 BLEU points improvement over a baseline system on the WMT21 triangular MT development set. In the official evalua- tion of the test set, ours is ranked 2nd in terms of BLEU scores.

## introduction we participate in the wmt21 triangular machine translation task, using the direct and indirect parallel data to improve russian-to-chinese machine translation. the provided data consists of one noisy web corpus (russian-chinese, direct translation) and two combined bitexts from several public resources (english-chinese/russian, indirect). such cases frequently occur in both actual translation services and research. in particular, this task is crucial in scenarios where we need to improve the performance of non-english translations or lowresource languages with high-resource parallel data. ## conclusion this paper depicts papago's submissions to the wmt21 triangular mt shared task. we have conducted extensive experiments using various techniques such as data filtering, data augmentation, model ensembling, and re-ranking in the triangular mt scenario. except for existing techniques, we also have tried to apply data filtering with labse sentence score and data augmentation using pivot language and demonstrated their effectiveness in translation performance. as a result, our system achieves the second record according to the released official results.