  We present a constituency parsing algorithm that, like a supertagger, works
by assigning labels to each word in a sentence. In order to maximally leverage
current neural architectures, the model scores each word's tags in parallel,
with minimal task-specific structure. After scoring, a left-to-right
reconciliation phase extracts a tree in (empirically) linear time. Our parser
achieves 95.4 F1 on the WSJ test set while also achieving substantial speedups
compared to current state-of-the-art parsers with comparable accuracies.
