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Explore structured learning algorithms in this advanced Natural Language Processing lecture from Carnegie Mellon University. Delve into reinforcement learning, minimum risk training, and the structured perceptron. Examine structured max-margin objectives and simple remedies to exposure bias. Learn about globally normalized models, sampling and beam search techniques, and various structured training approaches including hinge loss, cost-augmented hinge loss, and contrastive learning. Gain insights into teacher forcing, self-training, and evaluation metrics for structured prediction tasks in NLP.
