In this work, we focus on intrasentential code-mixing and propose several
different Synthetic Code-Mixing (SCM) data augmentation methods that outperform
the baseline on downstream sentiment analysis tasks across various amounts of
labeled gold data. Most importantly, our proposed methods demonstrate that
strategically replacing parts of sentences in the matrix language with a
constant mask significantly improves classification accuracy, motivating
further linguistic insights into the phenomenon of code-mixing. We test our
data augmentation method in a variety of low-resource and cross-lingual
settings, reaching up to a relative improvement of 7.73% on the extremely
scarce English-Malayalam dataset. We conclude that the code-switch pattern in
code-mixing sentences is also important for the model to learn. Finally, we
propose a language-agnostic SCM algorithm that is cheap yet extremely helpful
for low-resource languages.

本文研究围绕语内码混杂问题，提出了多种合成混杂数据方法，在各种数量的标注黄金数据中，在下游情感分析任务上表现出色。最重要的是，我们的方法表明，用定量掩码替换矩阵语言句子的部分内容可以显著提高分类准确性，这激发了对码混杂现象的进一步语言学洞察。我们在各种低资源和跨语言设置中测试了数据增强方法，在极度缺乏英马拉雅语的数据集上获得了高达 7.73％的相对改进。最后，我们提出了一种语言不可知的 SCM 算法，对低资源语言非常有用。