  We propose a novel linearization of a constituent tree, together with a new
locally normalized model. For each split point in a sentence, our model
computes the normalizer on all spans ending with that split point, and then
predicts a tree span from them. Compared with global models, our model is fast
and parallelizable. Different from previous local models, our linearization
method is tied on the spans directly and considers more local features when
performing span prediction, which is more interpretable and effective.
Experiments on PTB (95.8 F1) and CTB (92.4 F1) show that our model
significantly outperforms existing local models and efficiently achieves
competitive results with global models.
