Language Agnostic Code-Mixing Data Augmentation by Predicting Linguistic Patterns

TL;DR

This paper introduces language-agnostic synthetic data augmentation methods for code-mixed text, significantly improving sentiment analysis accuracy especially in low-resource settings by leveraging linguistic patterns and strategic masking.

Contribution

It proposes novel, language-agnostic SCM data augmentation techniques that outperform baselines and highlight the importance of linguistic patterns in code-mixing for NLP tasks.

Findings

Up to 7.73% improvement on English-Malayalam dataset

Strategic masking of sentence parts enhances classification accuracy

Language-agnostic SCM method benefits low-resource languages

Abstract

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.