  Most syntactic dependency parsing models may fall into one of two categories:
transition- and graph-based models. The former models enjoy high inference
efficiency with linear time complexity, but they rely on the stacking or
re-ranking of partially-built parse trees to build a complete parse tree and
are stuck with slower training for the necessity of dynamic oracle training.
The latter, graph-based models, may boast better performance but are
unfortunately marred by polynomial time inference. In this paper, we propose a
novel parsing order objective, resulting in a novel dependency parsing model
capable of both global (in sentence scope) feature extraction as in graph
models and linear time inference as in transitional models. The proposed global
greedy parser only uses two arc-building actions, left and right arcs, for
projective parsing. When equipped with two extra non-projective arc-building
actions, the proposed parser may also smoothly support non-projective parsing.
Using multiple benchmark treebanks, including the Penn Treebank (PTB), the
CoNLL-X treebanks, and the Universal Dependency Treebanks, we evaluate our
parser and demonstrate that the proposed novel parser achieves good performance
with faster training and decoding.
