This paper introduces the Dtranx AI translation system, developed for the WMT 2023 Universal Translation Shared Task. Our team participated in two language directions: English to Chinese and Chinese to English. Our primary focus was on enhancing the effectiveness of the Chinese-to-English model through the implementation of bilingual models. Our approach involved various techniques such as data corpus filtering, model size scaling, sparse expert models (especially the Transformer model with adapters), large-scale back-translation, and language model reordering. According to automatic evaluation, our system secured the first place in the English-to-Chinese category and the second place in the Chinese-to-English category.

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.