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State-of-the-art deep neural networks require large-scale labeled training data that is often expensive to obtain or not available for many tasks.
Making deep neural networks robust to label noise: A loss correction approach
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Learning from rules generalizing labeled exemplars
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Contextualized weak supervision for text classification
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A survey on recent approaches for natural language processing in low-resource scenarios
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