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arXiv · 1802.08545

Unsupervised Grammar Induction with Depth-bounded PCFG

Lifeng Jin·Finale Doshi-Velez·Timothy Miller·William Schuler·Lane Schwartz
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Abstract

There has been recent interest in applying cognitively or empirically motivated bounds on recursion depth to limit the search space of grammar induction models (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016). This work extends this depth-bounding approach to probabilistic context-free grammar induction (DB-PCFG), which has a smaller parameter space than hierarchical sequence models, and therefore more fully exploits the space reductions of depth-bounding. Results for this model on grammar acquisition from transcribed child-directed speech and newswire text exceed or are competitive with those of other models when evaluated on parse accuracy. Moreover, gram- mars acquired from this model demonstrate a consistent use of category labels, something which has not been demonstrated by other acquisition models.

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Lifeng Jin, Finale Doshi-Velez, Timothy Miller, William Schuler, Lane Schwartz. 2018-02-23. Unsupervised Grammar Induction with Depth-bounded PCFG. https://arxiv.org/abs/1802.08545

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