BERT-base

CCG directed types improve structural generalization on SLOG, surpassing AM-Parser on position-shift categories

A new arXiv preprint redesigns the symbolic backend of a semantic parsing system using CCG directed types with a deterministic CKY decoder and only 30K learnable parameters, achieving 75.9% LF exact match on the SLOG benchmark under BERT-base, surpassing the previous SOTA AM-Parser (70.8%). Gains are highly category-specific: the CCG system outperforms AM-Parser on all 5 position-shift categories (+29.9pp) while AM-Parser retains an edge on recursive-depth categories. Swapping in DeBERTa-v3-large as encoder pushes performance to 90.7%, with encoder gains complementing directionality gains in orthogonal category groupings. The work argues that directional representations shift the generalization bottleneck from the symbolic layer to the neural encoder, enabling further improvement through encoder scaling.