Neural Bi-Lexicalized PCFG Induction

Songlin Yang; Yanpeng Zhao; Kewei Tu

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arXiv · 2105.15021

Neural Bi-Lexicalized PCFG Induction

Songlin Yang·Yanpeng Zhao·Kewei Tu

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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.

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Songlin Yang, Yanpeng Zhao, Kewei Tu. 2021-05-31. Neural Bi-Lexicalized PCFG Induction. https://arxiv.org/abs/2105.15021

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