  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.
