Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
Explores neural model analysis in NLP, covering evaluation, probing, and ablation studies to understand model behavior and interpretability.
Delves into coreference resolution, discussing challenges, advancements, and evaluation methods.
Discusses the Dirichlet distribution, Bayesian inference, posterior mean and variance, conjugate priors, and predictive distribution in the Dirichlet-Multinomial model.
Introduces topic models, covering clustering, GMM, LDA, Dirichlet distribution, and variational inference.
Discusses the ethical implications of NLP systems, focusing on biases, toxicity, and privacy concerns in language models.
