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.

翻译：本文介绍了Stevens技术研究所为WMT 2022 共同任务:编码混合机器翻译(MixMT)提交的呈件,任务包括两个子任务,即向Hinglish提供1美元印地文/英文的子任务,向Hinglish提供2美元Hingrish的子任务,我们的调查结果在于通过使用大型预先培训的多语言NMT模型和内域数据集以及后译和连带技术改进了工作。翻译产出根据ROUGE-L和WER的参考翻译自动评价。根据ROUGE-L和WER,我们的系统在子任务2美元上达到1美元,根据WER和人类评价,在1美元子任务上达到1美元,根据WER和人类评价,在1美元上达到1美元,在ROUGE-L指标的子任务上达到1美元。