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There have been several recent attempts to improve the accuracy of grammarinduction systems by bounding the recursive complexity of the induction model(Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016; Jin et al.,2018). Modern depth-bounded grammar inducers have been shown to be moreaccurate than early unbounded PCFG inducers, but this technique has never beencompared against unbounded induction within the same system, in part becausemost previous depth-bounding models are built around sequence models, thecomplexity of which grows exponentially with the maximum allowed depth. Thepresent work instead applies depth bounds within a chart-based Bayesian PCFGinducer (Johnson et al., 2007b), where bounding can be switched on and off, andthen samples trees with and without bounding. Results show that depth-boundingis indeed significantly effective in limiting the search space of the inducerand thereby increasing the accuracy of the resulting parsing model. Moreover,parsing results on English, Chinese and German show that this bounded modelwith a new inference technique is able to produce parse trees more accuratelythan or competitively with state-of-the-art constituency-based grammarinduction models.

Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction