  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.
