  Compound probabilistic context-free grammars (C-PCFGs) have recently
established a new state of the art for phrase-structure grammar induction.
However, due to the high time-complexity of chart-based representation and
inference, it is difficult to investigate them comprehensively. In this work,
we rely on a fast implementation of C-PCFGs to conduct evaluation complementary
to that of~\citet{kim-etal-2019-compound}. We highlight three key findings: (1)
C-PCFGs are data-efficient, (2) C-PCFGs make the best use of global
sentence-level information in preterminal rule probabilities, and (3) the best
configurations of C-PCFGs on English do not always generalize to
morphology-rich languages.
