  Non-local features have been exploited by syntactic parsers for capturing
dependencies between sub output structures. Such features have been a key to
the success of state-of-the-art statistical parsers. With the rise of deep
learning, however, it has been shown that local output decisions can give
highly competitive accuracies, thanks to the power of dense neural input
representations that embody global syntactic information. We investigate two
conceptually simple local neural models for constituent parsing, which make
local decisions to constituent spans and CFG rules, respectively. Consistent
with previous findings along the line, our best model gives highly competitive
results, achieving the labeled bracketing F1 scores of 92.4% on PTB and 87.3%
on CTB 5.1.
