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CMU Advanced NLP: Structured Learning Algorithms

Graham Neubig via YouTube

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This advanced lecture examines structured learning algorithms for natural language processing, including structured perceptrons, max-margin objectives, reinforcement learning, and methods for reducing exposure bias.

Syllabus

Introduction
Types of prediction
Teacher forcing
Evaluation metrics
Structured prediction
Reminder
Globally normalized models
Sampling and Beam Search
Structured Perceptron
Global Structured Perceptron
Structured Training Pretraining
Hinge Loss
Cost Augmented Hinge Loss
Cost Over Sequences
Structured Hinge Loss
Label Smoothing vs Hinge Loss
Contrastive Learning
Teacher Forced
Selftraining

Taught by

Graham Neubig

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