This paper introduces WeChat's participation in WMT 2022 shared biomedical
translation task on Chinese to English. Our systems are based on the
Transformer, and use several different Transformer structures to improve the
quality of translation. In our experiments, we employ data filtering, data
generation, several variants of Transformer, fine-tuning and model ensemble.
Our Chinese$\to$English system, named Summer, achieves the highest BLEU score
among all submissions.

本文介绍了微信参与 WMT 2022 共享生物医学翻译任务的情况，我们基于 Transformer 模型构建系统，采用数据过滤、数据增强、多种 Transformer 变体、微调和模型集成等方法来提高翻译质量，其中我们的中文至英文系统名为 Summer，在所有提交作品中获得最高 BLEU 分数。

WMT22 生物医学翻译任务的微信神经机器翻译系统

Summer: WeChat Neural Machine Translation Systems for the WMT22 Biomedical Translation Task

Code-mixed machine translation has become an important task in multilingual
communities and extending the task of machine translation to code mixed data
has become a common task for these languages. In the shared tasks of WMT 2022,
we try to tackle the same for both English + Hindi to Hinglish and Hinglish to
English. The first task dealt with both Roman and Devanagari script as we had
monolingual data in both English and Hindi whereas the second task only had
data in Roman script. To our knowledge, we achieved one of the top ROUGE-L and
WER scores for the first task of Monolingual to Code-Mixed machine translation.
In this paper, we discuss the use of mBART with some special pre-processing and
post-processing (transliteration from Devanagari to Roman) for the first task
in detail and the experiments that we performed for the second task of
translating code-mixed Hinglish to monolingual English.

本文讨论了在 WMT 2022 共享任务中使用 mBART 进行处理特殊预处理和后处理（从 Devanagari 到 Roman 的音译），以解决单语到机器混合翻译的任务，并讨论了针对机器混合 Hinglish 到单语英语的翻译实验。