Lecture
Interpretability & Analysis: Modern NLP
Related lectures (35)
Introduction to NLP and the Course
Covers the basics of Natural Language Processing, including challenges, linguistic processing levels, and the impact of power laws.
Vision-Language-Action Models: Training and Applications
Delves into training and applications of Vision-Language-Action models, emphasizing large language models' role in robotic control and the transfer of web knowledge. Results from experiments and future research directions are highlighted.
Introduction to Natural Language Processing
Introduces the basics of Natural Language Processing, covering challenges, application domains, and linguistic processing levels.
Deep Learning for Question Answering
Explores deep learning for question answering, analyzing neural networks and model robustness to noise.
Parsing: CYK Algorithm
Explores formal grammars, parsing algorithms, CYK algorithm efficiency, and syntactic correctness in Natural Language Processing.
Modern NLP and Ethics in NLP
Delves into advancements and challenges in NLP, along with ethical considerations and potential harms.
Model Analysis
Explores neural model analysis in NLP, covering evaluation, probing, and ablation studies to understand model behavior and interpretability.
Syntactic Parsing: Dependency Structure
Covers syntactic structure, dependency parsing, and neural network transition-based parsing, highlighting the importance of dependency structure in linguistic analysis.
Words Tokens: Lexical Level Overview
Explores words, tokens, and language models in NLP, covering challenges in defining them, lexicon usage, n-grams, and probability estimation.
Neural Networks for NLP
Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.