Replacing MLP-based PCFG rule scoring with holographic circular-correlation operations on torus-constrained embeddings achieves state-of-the-art unsupervised parsing in six languages with 99.94% fewer rule-scoring parameters.
The Thirteenth International Conference on Learning Representations , year =
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2026 1verdicts
ACCEPT 1representative citing papers
citing papers explorer
-
Holographic Neural PCFG for Unsupervised Parsing
Replacing MLP-based PCFG rule scoring with holographic circular-correlation operations on torus-constrained embeddings achieves state-of-the-art unsupervised parsing in six languages with 99.94% fewer rule-scoring parameters.