CMU Neural Nets for NLP: Model Interpretation

The course covers model interpretation in the context of Neural Networks for Natural Language Processing. The learning outcomes include understanding the importance of interpretability, exploring different explanation techniques such as gradient-based importance scores and extractive rationale generation. The course teaches skills in probing sentence embeddings for linguistic properties and evaluating model interpretations. The teaching method is through a lecture format. The intended audience for this course is individuals interested in neural networks, natural language processing, and model interpretation.