Lan-Bridge MT ’s Participation in the WMT 2022 General Translation Shared Task (2022)

Abstract: This paper describes Lan-Bridge Translation systems for the WMT 2022 General Translation shared task. We participate in 18 language directions: English to and from Czech, German, Ukrainian, Japanese, Russian, Chinese, English to Croatian, French to German, Yakut to and from Russian and Ukrainian to and from Czech.To develop systems covering all these direc_x0002_tions, we mainly focus on multilingual mod_x0002_els. In general, we apply data corpus filtering, scaling model size, sparse expert model (in par_x0002_ticular, Transformer with adapters), large scale backtranslation and language model rerankingtechniques. Our system ranks first in 6 directions based on automatic evaluation.

## introduction our lan-bridge mt team participate in the wmt 2022 general translation shared task. as machine translation expands into more and more languages, multilingual machine translation has attracted more and more attention in both academia and industry. it can not only avoid training a separate model for each language pair but also transfer knowledge from high-resource languages to low-resource ones. many systems such as @xcite submitted in previous years have proved this point and achieved a state of the art results in some language directions. for data preprocessing, knowledge-based rules, language detection, and language model are involved to clean parallel data, monolingual data, and synthetic data (mainly from large-scale data mining and backtranslation). punctuation normalization and bpe (byte pair encoding) @xcite with subword regularization method @xcite are applied for all languages. as for models, we fork fairseq @xcite as our development tool and use transformer @xcite as the main architecture. in addition, we follow @xcite to extend transformer by adding language-specific adapters to bridge the gap between different language pairs. finally, we ensemble dense transformer models and sparse adapter models, and the final result are re-ranked by language models. for english to and from chinese, we develop a separate system. in addition to optimization techniques similar to multilingual models, we also use additional private data. and for yakut to and from russian, due to a smaller corpus, we simply apply fine-tuning and backtranslation on our multilingual models. we win the first place in russian ↔ yakut, @xmath0, @xmath1, @xmath2. foot_0 2 system overview ## experiment we conduct experiments to quantify the impact of each component in our system. the evaluation conduct on newstest2021 or development set on wmt22 using sacrebleu @xcite . ## conclusion in this paper, we described lan-bridge's submission to the wmt2022 general translation shared task. our main exploration was using a multilingual model to train different language pairs. it shows that the multilingual model can achieve state of art results in both high-and low-resource language directions. meanwhile, we found that the multilingual model worked better for languages from the same or close language families than languages from distant language families. finally, for extremely low-resource languages, even a multilingual model can boost their performance of them, but the translation is still far from usable.