  Syntactic parsing is a highly linguistic processing task whose parser
requires training on treebanks from the expensive human annotation. As it is
unlikely to obtain a treebank for every human language, in this work, we
propose an effective cross-lingual UD parsing framework for transferring parser
from only one source monolingual treebank to any other target languages without
treebank available. To reach satisfactory parsing accuracy among quite
different languages, we introduce two language modeling tasks into dependency
parsing as multi-tasking. Assuming only unlabeled data from target languages
plus the source treebank can be exploited together, we adopt a self-training
strategy for further performance improvement in terms of our multi-task
framework. Our proposed cross-lingual parsers are implemented for English,
Chinese, and 22 UD treebanks. The empirical study shows that our cross-lingual
parsers yield promising results for all target languages, for the first time,
approaching the parser performance which is trained in its own target treebank.
