  Treebank translation is a promising method for cross-lingual transfer of
syntactic dependency knowledge. The basic idea is to map dependency arcs from a
source treebank to its target translation according to word alignments. This
method, however, can suffer from imperfect alignment between source and target
words. To address this problem, we investigate syntactic transfer by code
mixing, translating only confident words in a source treebank. Cross-lingual
word embeddings are leveraged for transferring syntactic knowledge to the
target from the resulting code-mixed treebank. Experiments on University
Dependency Treebanks show that code-mixed treebanks are more effective than
translated treebanks, giving highly competitive performances among
cross-lingual parsing methods.
