Methods › Natural Language Processing › Language Models › mBART

mBART

66 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

mBART is a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective. The input texts are noised by masking phrases and permuting sentences, and a single Transformer model is learned to recover the texts. Different from other pre-training approaches for machine translation, mBART pre-trains a complete autoregressive Seq2Seq model. mBART is trained once for all languages, providing a set of parameters that can be fine-tuned for any of the language pairs in both supervised and unsupervised settings, without any task-specific or language-specific modifications or initialization schemes.

Source: Multilingual Denoising Pre-training for Neural Machine...

Papers archive 2025-07-28

30 shown of 66, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 76 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Translation38
Machine Translation28
Sentence13
Text Generation9
Decoder8
Denoising8
NMT8
Transfer Learning8
Abstractive Text Summarization7
Text Summarization7
Cross-Lingual Transfer5
Language Modeling5
Language Modelling5
Natural Language Understanding5
Text Simplification4
Articles3
Data Augmentation3
Headline Generation3
Question Generation3
Question-Generation3

Usage over time archive 2025-07-28

Papers per year tagged with mBART: 2020 to 2025, peak 18 18 0 2020: 7 papers 2020 2021: 15 papers 2021 2022: 14 papers 2022 2023: 18 papers 2023 2024: 10 papers 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (66 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Language Models

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