  Recently, neural network approaches for parsing have largely automated the
combination of individual features, but still rely on (often a larger number
of) atomic features created from human linguistic intuition, and potentially
omitting important global context. To further reduce feature engineering to the
bare minimum, we use bi-directional LSTM sentence representations to model a
parser state with only three sentence positions, which automatically identifies
important aspects of the entire sentence. This model achieves state-of-the-art
results among greedy dependency parsers for English. We also introduce a novel
transition system for constituency parsing which does not require binarization,
and together with the above architecture, achieves state-of-the-art results
among greedy parsers for both English and Chinese.
