Summary of Autoaugment Is What You Need: Enhancing Rule-based Augmentation Methods in Low-resource Regimes, by Juhwan Choi et al.
AutoAugment Is What You Need: Enhancing Rule-based Augmentation Methods in Low-resource Regimes
by Juhwan Choi, Kyohoon Jin, Junho Lee, Sangmin Song, Youngbin Kim
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes an innovative approach to text data augmentation, a crucial step in natural language processing (NLP) and machine learning (ML). The authors recognize the limitations of traditional rule-based methods, which may introduce semantic damage despite their simplicity. To mitigate this issue, they suggest adapting AutoAugment to enhance existing augmentation techniques. Experimental results demonstrate that this approach can improve upon state-of-the-art models and even boost cutting-edge pre-trained language models. Moreover, it provides a practical solution for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to make computers learn from text data more effectively. Right now, we have simple methods that work well but might cause some problems. A new idea called AutoAugment can help fix these issues and make language models even stronger. The results show that this approach works really well with existing models and can even improve the best ones out there. |
Keywords
» Artificial intelligence » Data augmentation » Machine learning » Natural language processing » Nlp
