Lecture
This lecture discusses the pretraining of sequence-to-sequence models, focusing on BART and T5. It begins by reviewing the concepts of transfer learning and fine-tuning, emphasizing the importance of pretraining in natural language processing (NLP). The instructor explains how models like ELMO, BERT, and GPT have transformed NLP by enabling the learning of contextual embeddings. The lecture highlights the differences between pretraining and fine-tuning stages, detailing the requirements for data and the simplicity of training objectives during pretraining. The instructor introduces BART, a model that combines elements of BERT and GPT, and explains its architecture, including the bidirectional encoder and autoregressive decoder. Various corruption strategies for input data are discussed, showcasing how they enhance the model's performance. The lecture concludes with T5, a model that extends the sequence-to-sequence framework to various NLP tasks, demonstrating its effectiveness through extensive pretraining on large datasets. Overall, the lecture provides a comprehensive overview of modern approaches to pretraining in NLP.