styfeng/DataAug4NLP
Collection of papers and resources for data augmentation for NLP.
This is a curated collection of research papers and resources focused on improving natural language processing (NLP) models. It helps researchers and practitioners find effective strategies to boost model performance when training data is limited. The collection provides specific techniques, categorized by NLP tasks like text classification or machine translation, to generate more diverse training examples from existing datasets.
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Use this if you are an NLP researcher or practitioner looking for proven data augmentation techniques to improve the performance of your text classification, translation, summarization, or other NLP models, especially when you have small datasets.
Not ideal if you are looking for ready-to-use software libraries or code implementations without wanting to explore the underlying research papers.
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Aug 12, 2022
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