Code-switching (CSW) text generation has been receiving increasing attention
as a solution to address data scarcity. In light of this growing interest, we
need more comprehensive studies comparing different augmentation approaches. In
this work, we compare three popular approaches: lexical replacements,
linguistic theories, and back-translation (BT), in the context of Egyptian
Arabic-English CSW. We assess the effectiveness of the approaches on machine
translation and the quality of augmentations through human evaluation. We show
that BT and CSW predictive-based lexical replacement, being trained on CSW
parallel data, perform best on both tasks. Linguistic theories and random
lexical replacement prove to be effective in the lack of CSW parallel data,
where both approaches achieve similar results.

在埃及阿拉伯语 - 英语混合代码生成中，我们比较了词汇替换、语言理论和回译三种流行的增强方法，通过人工评估评估了这些方法在机器翻译上的效果和增强质量。结果显示，基于回译和基于代码交替预测的词汇替换方法在两个任务中表现最佳，而在缺少代码交替并行数据的情况下，语言理论和随机词汇替换方法实现了类似的结果。