Methods › Natural Language Processing › Copy Mechanisms › TopK Copy
TopK Copy
Introduced by Kung-Hsiang Huang et al. in Document-level Entity-based Extraction as Template Generation
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
TopK Copy is a cross-attention guided copy mechanism for entity extraction where only the Top-k important attention heads are used for computing copy distributions. The motivation is that that attention heads may not equally important, and that some heads can be pruned out with a marginal decrease in overall performance. Attention probabilities produced by insignificant attention heads may be noisy. Thus, computing copy distributions without these heads could improve the model’s ability to infer the importance of each token in the input document.
Papers archive 2025-07-28
1 shown of 1, 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.
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Document-level Entity-based Extraction as Template Generation 10 Sep 2021 · 1 repository · arXiv:2109.04901Syntology ran 0 of 10 samples · 10 unverified
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
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
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