This paper explores the task of leveraging typology in the context of
cross-lingual dependency parsing. While this linguistic information has shown
great promise in pre-neural parsing, results for neural architectures have been
mixed. The aim of our investigation is to better understand this
state-of-the-art. Our main findings are as follows: 1) The benefit of
typological information is derived from coarsely grouping languages into
syntactically-homogeneous clusters rather than from learning to leverage
variations along individual typological dimensions in a compositional manner;
2) Typology consistent with the actual corpus statistics yields better transfer
performance; 3) Typological similarity is only a rough proxy of cross-lingual
transferability with respect to parsing.

本研究探讨了在跨语言依赖分析的背景下，如何利用类型学来提高性能，而我们的主要发现是将语言聚合成句法同质性群组有助于性能，而将类型维度进行组合式学习的效果并不显著。同时，考虑到所选择语料库的列统计学与类型学相一致时，性能表现也将更为出色。