Summary of Inditext Boost: Text Augmentation For Low Resource India Languages, by Onkar Litake et al.
IndiText Boost: Text Augmentation for Low Resource India Languages
by Onkar Litake, Niraj Yagnik, Shreyas Labhsetwar
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to addressing the issue of data scarcity in low-resource languages, particularly focusing on six Indian languages: Sindhi, Marathi, Hindi, Gujarati, Telugu, and Sanskrit. The authors draw upon existing work on English language text augmentation and adapt techniques like Easy Data Augmentation, Back Translation, Paraphrasing, Text Generation using LLMs, and Text Expansion using LLMs for text classification tasks. By employing these strategies, the paper aims to bridge the gap in text augmentation research for Indian languages. The authors’ surprising findings suggest that basic data augmentation techniques outperform Large Language Models (LLMs) in certain scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding new ways to solve a big problem: we don’t have enough data to train machines to understand many languages, especially Indian languages like Sindhi, Marathi, Hindi, Gujarati, Telugu, and Sanskrit. The authors are trying to fix this by taking ideas from how people augment English text data and adapting them for these Indian languages. They’re testing different techniques to see which ones work best for classifying text into categories. The results show that simple techniques can actually do a better job than advanced computer models in some cases! |
Keywords
* Artificial intelligence * Data augmentation * Text classification * Text generation * Translation
