  We propose two fast neural combinatory models for constituency parsing:
binary and multi-branching. Our models decompose the bottom-up parsing process
into 1) classification of tags, labels, and binary orientations or chunks and
2) vector composition based on the computed orientations or chunks. These
models have theoretical sub-quadratic complexity and empirical linear
complexity. The binary model achieves an F1 score of 92.54 on Penn Treebank,
speeding at 1327.2 sents/sec. Both the models with XLNet provide near
state-of-the-art accuracies for English. Syntactic branching tendency and
headedness of a language are observed during the training and inference
processes for Penn Treebank, Chinese Treebank, and Keyaki Treebank (Japanese).
