  We propose a novel in-order chart-based model for constituent parsing.
Compared with previous CKY-style and top-down models, our model gains
advantages from in-order traversal of a tree (rich features, lookahead
information and high efficiency) and makes a better use of structural knowledge
by encoding the history of decisions. Experiments on the Penn Treebank show
that our model outperforms previous chart-based models and achieves competitive
performance compared with other discriminative single models.
