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Explore advanced natural language processing concepts through this comprehensive lecture series from Carnegie Mellon University taught by Graham Neubig. Master fundamental NLP techniques starting with text classification and language modeling using neural networks, then progress to sophisticated architectures including recurrent neural networks, attention mechanisms, and pre-training methods. Delve into cutting-edge topics such as multi-task learning across domains and languages, prompting strategies, and sequence-to-sequence pre-training approaches. Learn to interpret and debug NLP models while gaining expertise in word segmentation, morphology, and experimental design for NLP research. Study syntactic and semantic parsing techniques, develop text-based question answering systems, and build dialog systems. Examine critical issues of bias and fairness in computational social science applications, and explore advanced methodologies including knowledge base integration, document-level modeling, reinforcement learning, structured learning algorithms, latent variable models, and adversarial learning techniques.
