What is recorded here?
Pretrained a sequence-to-sequence transformer by corrupting text and reconstructing it, reporting strong generation and comprehension transfer.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The 2019 preprint was later published at ACL 2020; the paper does not invent denoising autoencoders or transformer encoder-decoders.
Read the original work
- Lewis and colleagues: BART denoising seq2seq pre-training — original paper
- Lewis and colleagues: BART denoising seq2seq pre-training — official research page
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Mike Lewis; Yinhan Liu; Naman Goyal; Marjan Ghazvininejad; Abdelrahman Mohamed; Omer Levy; Ves Stoyanov; Luke Zettlemoyer
Complete named paper byline. Human research, source-reported system execution and experimental validation are distinct; individual effort shares are not measured.
Research locations and affiliations
Location not yet established
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English catalogue narrative
Lewis and colleagues: BART denoising seq2seq pre-training
Pretrained a sequence-to-sequence transformer by corrupting text and reconstructing it, reporting strong generation and comprehension transfer.
Scientific scope
The 2019 preprint was later published at ACL 2020; the paper does not invent denoising autoencoders or transformer encoder-decoders.
Reading status
This 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.
People and systems
Mike Lewis; Yinhan Liu; Naman Goyal; Marjan Ghazvininejad; Abdelrahman Mohamed; Omer Levy; Ves Stoyanov; Luke Zettlemoyer
Complete named paper byline. Human research, source-reported system execution and experimental validation are distinct; individual effort shares are not measured.