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In this work, we focus on intrasentential code-mixing and propose severaldifferent Synthetic Code-Mixing (SCM) data augmentation methods that outperformthe baseline on downstream sentiment analysis tasks across various amounts oflabeled gold data. Most importantly, our proposed methods demonstrate thatstrategically replacing parts of sentences in the matrix language with aconstant mask significantly improves classification accuracy, motivatingfurther linguistic insights into the phenomenon of code-mixing. We test ourdata augmentation method in a variety of low-resource and cross-lingualsettings, reaching up to a relative improvement of 7.73% on the extremelyscarce English-Malayalam dataset. We conclude that the code-switch pattern incode-mixing sentences is also important for the model to learn. Finally, wepropose a language-agnostic SCM algorithm that is cheap yet extremely helpfulfor low-resource languages.

Language Agnostic Code-Mixing Data Augmentation by Predicting Linguistic Patterns