Pretraining Sequence-to-Sequence Models: BART + T5

This lecture covers the pretraining of sequence-to-sequence models using BART and T5, discussing the concepts of transfer learning, fine-tuning, and the specific architectures of BART and T5. It explains the pretraining process, corruption strategies, and the tasks these models can perform, such as classification and sequence labeling. The lecture also compares the performance of BART and T5 with other models like BERT, UniLM, and XLNet, highlighting their capabilities in various tasks. Additionally, it explores the results of summarization tasks and the references for further reading on the topic.