We explore the link between the extent to which syntactic relations are
preserved in translation and the ease of correctly constructing a parse tree in
a zero-shot setting. While previous work suggests such a relation, it tends to
focus on the macro level and not on the level of individual edges-a gap we aim
to address. As a test case, we take the transfer of Universal Dependencies (UD)
parsing from English to a diverse set of languages and conduct two sets of
experiments. In one, we analyze zero-shot performance based on the extent to
which English source edges are preserved in translation. In another, we apply
three linguistically motivated transformations to UD, creating more
cross-lingually stable versions of it, and assess their zero-shot parsability.
In order to compare parsing performance across different schemes, we perform
extrinsic evaluation on the downstream task of cross-lingual relation
extraction (RE) using a subset of a popular English RE benchmark translated to
Russian and Korean. In both sets of experiments, our results suggest a strong
relation between cross-lingual stability and zero-shot parsing performance.

本文研究在翻译过程中保留句法关系的程度与零样本情况下正确构造解析树的难度之间的联系，通过将 Universal Dependencies 从英语传输到不同的语言进行测试，结果表明跨语稳定性与零样本解析性能之间存在强烈的关联。

关于句法差异与零 - shot 表现之间的关系

On the Relation between Syntactic Divergence and Zero-Shot Performance

Universal Dependencies is an open community effort to create
cross-linguistically consistent treebank annotation for many languages within a
dependency-based lexicalist framework. The annotation consists in a
linguistically motivated word segmentation; a morphological layer comprising
lemmas, universal part-of-speech tags, and standardized morphological features;
and a syntactic layer focusing on syntactic relations between predicates,
arguments and modifiers. In this paper, we describe version 2 of the guidelines
(UD v2), discuss the major changes from UD v1 to UD v2, and give an overview of
the currently available treebanks for 90 languages.

该研究介绍了 Universal Dependencies 的第二个版本，其中包括词汇分析、词形还原、标准化标记和句法关系，适用于 90 种语言，以及主要变化（UD v1 到 UD v2）的讨论。

通用依存关系 v2：一个不断增长的多语言树库集合

Universal Dependencies v2: An Evergrowing Multilingual Treebank  Collection

One of the most complex syntactic representations used in computational
linguistics and NLP are discontinuous constituent trees, crucial for
representing all grammatical phenomena of languages such as German. Recent
advances in dependency parsing have shown that Pointer Networks excel in
efficiently parsing syntactic relations between words in a sentence. This kind
of sequence-to-sequence models achieve outstanding accuracies in building
non-projective dependency trees, but its potential has not been proved yet on a
more difficult task. We propose a novel neural network architecture that, by
means of Pointer Networks, is able to generate the most accurate discontinuous
constituent representations to date, even without the need of Part-of-Speech
tagging information. To do so, we internally model discontinuous constituent
structures as augmented non-projective dependency structures. The proposed
approach achieves state-of-the-art results on the two widely-used NEGRA and
TIGER benchmarks, outperforming previous work by a wide margin.

通过 Pointer Networks，我们提出了一种新的神经网络架构，可以生成到目前为止最准确的不连续成分表示，即使不需要词性标注信息，此方法在 NEGRA 和 TIGER 基准测试中均取得了最新的最优结果。