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。

Gui at MixMT 2022: 英印混杂数据的机器翻译方法

Gui at MixMT 2022 : English-Hinglish: An MT approach for translation of code mixed data

This paper describes the Stevens Institute of Technology's submission for the
WMT 2022 Shared Task: Code-mixed Machine Translation (MixMT). The task
consisted of two subtasks, subtask $1$ Hindi/English to Hinglish and subtask
$2$ Hinglish to English translation. Our findings lie in the improvements made
through the use of large pre-trained multilingual NMT models and in-domain
datasets, as well as back-translation and ensemble techniques. The translation
output is automatically evaluated against the reference translations using
ROUGE-L and WER. Our system achieves the $1^{st}$ position on subtask $2$
according to ROUGE-L, WER, and human evaluation, $1^{st}$ position on subtask
$1$ according to WER and human evaluation, and $3^{rd}$ position on subtask $1$
with respect to ROUGE-L metric.

本论文介绍了史蒂文斯理工学院提交的 WMT 2022 共享任务：混合代码机器翻译并针对该任务的两个子任务的技术改进，包括采用大型预训练多语言 NMT 模型和领域内数据集以及反向翻译和集成技术等，该系统取得了子任务 2 上的第一名和子任务 1 上的第一和第三名。