Learning with Limited Labeled Data for Natural Language Understanding

IntroductionSupervised machine learning models need large amounts of labeled data. The more data you use to train your model, the better it performs. With the advent of deep learning-based models, the demand for data has increased by orders of magnitude. State-of-the-art deep learning NLP (Natural Language Processing) models typically have several hundred million parameters. For example, the BERT (Bidirectional Encoder Representations from Transformers) large model has 345 million parameters. Ho...

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