What is recorded here?
Combined sliding-window attention and task-selected global tokens to process long documents; extended the design to encoder-decoder tasks.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Linear scaling assumes fixed local-window size and bounded global tokens. The December revision adds LED within the same report package.
Read the original work
- Longformer: The Long-Document Transformer
- Beltagy and colleagues: Longformer local and global sparse attention — author or proceedings record
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
Iz Beltagy; Matthew E. Peters; Arman Cohan
Source-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.
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English catalogue narrative
Beltagy and colleagues: Longformer local and global sparse attention
Combined sliding-window attention and task-selected global tokens to process long documents; extended the design to encoder-decoder tasks.
Scientific scope
Linear scaling assumes fixed local-window size and bounded global tokens. The December revision adds LED within the same report package.
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
Iz Beltagy; Matthew E. Peters; Arman Cohan
Source-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.