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

In this paper, we propose a novel writer-independent global feature
extraction framework for the task of automatic signature verification which
aims to make robust systems for automatically distinguishing negative and
positive samples. Our method consists of an autoencoder for modeling the sample
space into a fixed length latent space and a Siamese Network for classifying
the fixed-length samples obtained from the autoencoder based on the reference
samples of a subject as being "Genuine" or "Forged." During our experiments,
usage of Attention Mechanism and applying Downsampling significantly improved
the accuracy of the proposed framework. We evaluated our proposed framework
using SigWiComp2013 Japanese and GPDSsyntheticOnLineOffLineSignature datasets.
On the SigWiComp2013 Japanese dataset, we achieved 8.65% EER that means 1.2%
relative improvement compared to the best-reported result. Furthermore, on the
GPDSsyntheticOnLineOffLineSignature dataset, we achieved average EERs of 0.13%,
0.12%, 0.21% and 0.25% respectively for 150, 300, 1000 and 2000 test subjects
which indicates improvement of relative EER on the best-reported result by
95.67%, 95.26%, 92.9% and 91.52% respectively. Apart from the accuracy gain,
because of the nature of our proposed framework which is based on neural
networks and consequently is as simple as some consecutive matrix
multiplications, it has less computational cost than conventional methods such
as DTW and could be used concurrently on devices such as GPU, TPU, etc.

本文提出了一种新颖的、面向自动签名验证任务的独立于作者的全局特征提取框架，其中包括一个自编码器和一个连体网络来实现样本的建模和分类，利用注意机制和降采样的技术，提高了框架的准确度，其简单性使其计算成本低，并且可以在 GPU、TPU 等设备上并行使用。在两个数据集上的实验表明，该方法相对于最佳报告结果取得了显著的准确度提高。