  There have been several recent attempts to improve the accuracy of grammar
induction 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 more
accurate than early unbounded PCFG inducers, but this technique has never been
compared against unbounded induction within the same system, in part because
most previous depth-bounding models are built around sequence models, the
complexity of which grows exponentially with the maximum allowed depth. The
present work instead applies depth bounds within a chart-based Bayesian PCFG
inducer (Johnson et al., 2007b), where bounding can be switched on and off, and
then samples trees with and without bounding. Results show that depth-bounding
is indeed significantly effective in limiting the search space of the inducer
and thereby increasing the accuracy of the resulting parsing model. Moreover,
parsing results on English, Chinese and German show that this bounded model
with a new inference technique is able to produce parse trees more accurately
than or competitively with state-of-the-art constituency-based grammar
induction models.
