We explore whether it is possible to build lighter parsers, that are
statistically equivalent to their corresponding standard version, for a wide
set of languages showing different structures and morphologies. As testbed, we
use the Universal Dependencies and transition-based dependency parsers trained
on feed-forward networks. For these, most existing research assumes de facto
standard embedded features and relies on pre-computation tricks to obtain
speed-ups. We explore how these features and their size can be reduced and
whether this translates into speed-ups with a negligible impact on accuracy.
The experiments show that grand-daughter features can be removed for the
majority of treebanks without a significant (negative or positive) LAS
difference. They also show how the size of the embeddings can be notably
reduced.

本文研究在保证依存分析精度不受影响的情况下，如何去除嵌入式特征和减小其规模，以构建适用于多种不同语言的轻量级解析器，并在 Universal Dependencies 数据集上进行了实验。实验证明，对于多数树库而言，可以去除 grand-daughter 特征且不产生显著差异，同时也证明了可以显著降低嵌入向量的大小。

使用轻量级前馈神经网络的基于转移的句法分析

Transition-based Parsing with Lighter Feed-Forward Networks

We propose a technique for learning representations of parser states in
transition-based dependency parsers. Our primary innovation is a new control
structure for sequence-to-sequence neural networks---the stack LSTM. Like the
conventional stack data structures used in transition-based parsing, elements
can be pushed to or popped from the top of the stack in constant time, but, in
addition, an LSTM maintains a continuous space embedding of the stack contents.
This lets us formulate an efficient parsing model that captures three facets of
a parser's state: (i) unbounded look-ahead into the buffer of incoming words,
(ii) the complete history of actions taken by the parser, and (iii) the
complete contents of the stack of partially built tree fragments, including
their internal structures. Standard backpropagation techniques are used for
training and yield state-of-the-art parsing performance.

该文提出了一种新的序列到序列神经网络控制结构 —— 堆栈 LSTM，用于学习过渡式依赖解析器的解析状态，将其应用于解析模型中，实现了解析器状态的三个方面，提高了解析性能。