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Efficient Semi-supervised Consistency Training for Natural Language Understanding
Manually labeled training data is expensive, noisy, and often scarce, such as when developing new features or localizing existing features for a new region.
Influence Scores at Scale for Efficient Language Data Sampling
Modern ML systems ingest data aggregated from diverse sources, such as synthetic, human-annotated, and live customer traffic.
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