{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bart-denoising-sequence-to-sequence-pre","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","arxiv_id":"1910.13461","date":"2019-10-29","proceeding":"ACL 2020 6","authors":["Mike Lewis","Yinhan Liu","Naman Goyal","Marjan Ghazvininejad","Abdel-rahman Mohamed","Omer Levy","Ves Stoyanov","Luke Zettlemoyer"],"abstract":"We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. 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