BART is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
  the left.

Sequence-to-sequence model with an encoder and a decoder. Encoder is fed a corrupted version of the tokens, decoder is fed the original tokens (but has a mask to hide the future words like a regular transformers decoder). A composition of the following transformations are applied on the pretraining tasks for the encoder:

mask random tokens (like in BERT)