Transformational Priors Over Grammars

Abstract

This paper proposes a novel class of PCFG parameterizations that support linguistically reasonable priors over PCFGs. To estimate the parameters is to discover a notion of relatedness among context-free rules such that related rules tend to have related probabilities. The prior favors grammars in which the relationships are simple to describe and have few major exceptions. A basic version that bases relatedness on weighted edit distance yields superior smoothing of grammars learned from the Penn Treebank (20% reduction of rule perplexity over the best previous method).