Deep Learning for Question Answering

This lecture covers the application of deep learning in question answering, focusing on models like BERT and their practical implications. It explores the use of pre-trained language models and their impact on natural language processing tasks. The lecture delves into the analysis of neural networks, including probing techniques to understand model behavior and the evaluation of model robustness to noise. Additionally, it discusses the importance of structured probing studies to uncover how models process inputs and the emergence of interpretable structures within neural networks.