A multi-head attention model for Russian morphological tagging supports open dictionaries via subtoken splitting and reports 98-99% accuracy on grammatical categories while running efficiently on consumer hardware.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
This survey discusses key components and challenges for Personal LLM Agents and reviews solutions for their capability, efficiency, and security.
citing papers explorer
-
A Multi-head-based architecture for effective morphological tagging in Russian with open dictionary
A multi-head attention model for Russian morphological tagging supports open dictionaries via subtoken splitting and reports 98-99% accuracy on grammatical categories while running efficiently on consumer hardware.
-
Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security
This survey discusses key components and challenges for Personal LLM Agents and reviews solutions for their capability, efficiency, and security.