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.

本文提出了一种能够同时进行全局特征提取和线性时间推理的依存句法分析模型，支持投影和非投影句子的解析，经检验表现良好。

全局贪心依存句法分析

Global Greedy Dependency Parsing

Starting from NMT, encoder-decoder neu- ral networks have been used for many
NLP problems. Graph-based models and transition-based models borrowing the en-
coder components achieve state-of-the-art performance on dependency parsing and
constituent parsing, respectively. How- ever, there has not been work
empirically studying the encoder-decoder neural net- works for transition-based
parsing. We apply a simple encoder-decoder to this end, achieving comparable
results to the parser of Dyer et al. (2015) on standard de- pendency parsing,
and outperforming the parser of Vinyals et al. (2015) on con- stituent parsing.

本研究针对基于转移的句法分析任务，探讨利用编解码神经网络进行建模的方法，并且取得了与当前最优方法相近的效果，同时在某些情况下表现更好。

编码器解码器移位归约句法分析

Encoder-Decoder Shift-Reduce Syntactic Parsing

Transition-based models can be fast and accurate for constituent parsing.
Compared with chart-based models, they leverage richer features by extracting
history information from a parser stack, which spans over non-local
constituents. On the other hand, during incremental parsing, constituent
information on the right hand side of the current word is not utilized, which
is a relative weakness of shift-reduce parsing. To address this limitation, we
leverage a fast neural model to extract lookahead features. In particular, we
build a bidirectional LSTM model, which leverages the full sentence information
to predict the hierarchy of constituents that each word starts and ends. The
results are then passed to a strong transition-based constituent parser as
lookahead features. The resulting parser gives 1.3% absolute improvement in WSJ
and 2.3% in CTB compared to the baseline, given the highest reported accuracies
for fully-supervised parsing.

本文针对移位 - 归约分析的相对弱点，利用双向 LSTM 模型提取向前追望特征，并将结果传递给强大的过渡性成分分析器，提高了 1.3% 在 WSJ 和 2.3% 在 CTB 中的准确性。