  We propose a novel constituency parsing model that casts the parsing problem
into a series of pointing tasks. Specifically, our model estimates the
likelihood of a span being a legitimate tree constituent via the pointing score
corresponding to the boundary words of the span. Our parsing model supports
efficient top-down decoding and our learning objective is able to enforce
structural consistency without resorting to the expensive CKY inference. The
experiments on the standard English Penn Treebank parsing task show that our
method achieves 92.78 F1 without using pre-trained models, which is higher than
all the existing methods with similar time complexity. Using pre-trained BERT,
our model achieves 95.48 F1, which is competitive with the state-of-the-art
while being faster. Our approach also establishes new state-of-the-art in
Basque and Swedish in the SPMRL shared tasks on multilingual constituency
parsing.
