Neural Bi-Lexicalized PCFG Induction

Abstract

Neural lexicalized PCFGs (L-PCFGs) have been shown effective in grammar induction. However, to reduce computational complexity, they make a strong independence assumption on the generation of the child word and thus bilexical dependencies are ignored. In this paper, we propose an approach to parameterize L-PCFGs without making implausible independence assumptions. Our approach directly models bilexical dependencies and meanwhile reduces both learning and representation complexities of L-PCFGs. Experimental results on the English WSJ dataset confirm the effectiveness of our approach in improving both running speed and unsupervised parsing performance.

arXiv commentTo appear in ACL 2021 main conference

References in corpus (4)

• Visually Grounded Neural Syntax Acquisition
• Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction
• StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling
• Left-corner Methods for Syntactic Modeling with Universal Structural Constraints