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      "statement": "# mBART: multilingual full-sequence denoising for translation transfer\n\nPretrained a complete shared encoder–decoder on corrupted multilingual text using span masking and sentence permutation, then adapted the initialization to supervised and unsupervised translation.\n\n## Scientific scope\n\nInherits BART's denoising and Transformer architecture. The contribution is multilingual full-sequence pretraining and transfer study, not invention of denoising or translation. Gains are data-resource and language-pair dependent; original25-language model is not later expanded releases.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nYinhan Liu; Jiatao Gu; Naman Goyal; Xian Li; Sergey Edunov; Marjan Ghazvininejad; Mike Lewis; Luke Zettlemoyer\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210v1)\n",
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