  We present BART, a denoising autoencoder for pretraining sequence-to-sequence
models. BART is trained by (1) corrupting text with an arbitrary noising
function, and (2) learning a model to reconstruct the original text. It uses a
standard Tranformer-based neural machine translation architecture which,
despite its simplicity, can be seen as generalizing BERT (due to the
bidirectional encoder), GPT (with the left-to-right decoder), and many other
more recent pretraining schemes. We evaluate a number of noising approaches,
finding the best performance by both randomly shuffling the order of the
original sentences and using a novel in-filling scheme, where spans of text are
replaced with a single mask token. BART is particularly effective when fine
tuned for text generation but also works well for comprehension tasks. It
matches the performance of RoBERTa with comparable training resources on GLUE
and SQuAD, achieves new state-of-the-art results on a range of abstractive
dialogue, question answering, and summarization tasks, with gains of up to 6
ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system
for machine translation, with only target language pretraining. We also report
ablation experiments that replicate other pretraining schemes within the BART
framework, to better measure which factors most influence end-task performance.
