Lecture
This lecture provides an in-depth overview of contextual representations in natural language processing, focusing on ELMO and BERT. It begins with an introduction to GPT, explaining its architecture and training methodology, including the use of masked multi-headed self-attention and the significance of pretraining on large corpora. The instructor discusses the fine-tuning process of these models for specific tasks, highlighting the improvements achieved in various benchmarks. The lecture then transitions to ELMO, detailing its bidirectional LSTM architecture and how it generates contextual embeddings. The instructor explains the advantages of ELMO over traditional word embeddings and its application in different tasks. Following this, BERT is introduced, showcasing its transformer encoder architecture and training techniques, including masked language modeling and next sentence prediction. The lecture concludes with a discussion on the advancements made by BERT and its variants, emphasizing the importance of contextualized embeddings in enhancing the performance of NLP models across various tasks.