  We describe a cross-lingual adaptation method based on syntactic parse trees
obtained from the Universal Dependencies (UD), which are consistent across
languages, to develop classifiers in low-resource languages. The idea of UD
parsing is to capture similarities as well as idiosyncrasies among
typologically different languages. In this paper, we show that models trained
using UD parse trees for complex NLP tasks can characterize very different
languages. We study two tasks of paraphrase identification and semantic
relation extraction as case studies. Based on UD parse trees, we develop
several models using tree kernels and show that these models trained on the
English dataset can correctly classify data of other languages e.g. French,
Farsi, and Arabic. The proposed approach opens up avenues for exploiting UD
parsing in solving similar cross-lingual tasks, which is very useful for
languages that no labeled data is available for them.
