Contextual Representations: ELMo & BERT

This lecture covers the evolution of contextualized embeddings in NLP, starting with ELMo and progressing to BERT. ELMo utilizes bidirectional LSTMs to generate word embeddings, while BERT enhances this with masked language modeling. The presentation includes detailed explanations of the training processes, architecture, and applications of both models, highlighting their significant impact on various NLP tasks. The instructor also discusses the challenges and improvements brought by models like DistilBERT and ELECTRA, showcasing the continuous advancements in contextual representation learning.