Bart : Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

The document discusses BART, a sequence-to-sequence model that uses denoising as a pre-training objective for various NLP tasks including natural language generation, translation, and comprehension. It highlights the architecture of BART, illustrating its bidirectional transformer encoder and autoregressive decoder, along with performance results across different natural language processing benchmarks. Additionally, it covers the training methodology, including techniques for noise introduction during pre-training and fine-tuning processes.