Language Agnostic Code-Mixing Data Augmentation by Predicting Linguistic Patterns
This addresses sentiment analysis for low-resource languages with code-mixing, but it is incremental as it builds on existing data augmentation techniques.
The paper tackled improving sentiment analysis for code-mixed text by proposing synthetic data augmentation methods, achieving up to a 7.73% relative improvement on a low-resource English-Malayalam dataset.
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.