IndiText Boost: Text Augmentation for Low Resource India Languages
Onkar Litake Niraj Yagnik Shreyas Labhsetwar
Text Augmentation is an important task for low-resource languages. It helps
deal with the problem of data scarcity. A data augmentation strategy is used to
deal with the problem of data scarcity. Through the years, much work has been
done on data augmentation for the English language. In contrast, very less work
has been done on Indian languages. This is contrary to the fact that data
augmentation is used to deal with data scarcity. In this work, we focus on
implementing techniques like Easy Data Augmentation, Back Translation,
Paraphrasing, Text Generation using LLMs, and Text Expansion using LLMs for
text classification on different languages. We focus on 6 Indian languages
namely: Sindhi, Marathi, Hindi, Gujarati, Telugu, and Sanskrit. According to
our knowledge, no such work exists for text augmentation on Indian languages.
We carry out binary as well as multi-class text classification to make our
results more comparable. We get surprising results as basic data augmentation
techniques surpass LLMs.