Methods › Natural Language Processing › Subword Segmentation › GBST
Gradient-based Subword Tokenization Module
GBST
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
GBST, or Gradient-based Subword Tokenization Module, is a soft gradient-based subword tokenization module that automatically learns latent subword representations from characters in a data-driven fashion. Concretely, GBST enumerates candidate subword blocks and learns to score them in a position-wise fashion using a block scoring network.
GBST learns a position-wise soft selection over candidate subword blocks by scoring them with a scoring network. In contrast to prior tokenization-free methods, GBST learns interpretable latent subwords, which enables easy inspection of lexical representations and is more efficient than other byte-based models.
Papers archive 2025-07-28
The archive tags no paper with this method.
Tasks archive 2025-07-28
The archive attaches no task to a paper tagged with this method.
Usage over time archive 2025-07-28
No dated papers to chart.
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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections