The University of Edinburgh participated in the WMT22 shared task on
code-mixed translation. This consists of two subtasks: i) generating code-mixed
Hindi/English (Hinglish) text generation from parallel Hindi and English
sentences and ii) machine translation from Hinglish to English. As both
subtasks are considered low-resource, we focused our efforts on careful data
generation and curation, especially the use of backtranslation from monolingual
resources. For subtask 1 we explored the effects of constrained decoding on
English and transliterated subwords in order to produce Hinglish. For subtask
2, we investigated different pretraining techniques, namely comparing simple
initialisation from existing machine translation models and aligned
augmentation. For both subtasks, we found that our baseline systems worked
best. Our systems for both subtasks were one of the overall top-performing
submissions.

该研究旨在解决低资源条件下代码混合翻译的问题，在数据生成和典藏方面投入了大量精力，通过限制解码效果得出最佳翻译结果，使用现有的机器翻译模型和对齐增强的预训练技术进行探索，最终实现了对应子任务最佳表现。