  Cross-lingual transfer is an effective way to build syntactic analysis tools
in low-resource languages. However, transfer is difficult when transferring to
typologically distant languages, especially when neither annotated target data
nor parallel corpora are available. In this paper, we focus on methods for
cross-lingual transfer to distant languages and propose to learn a generative
model with a structured prior that utilizes labeled source data and unlabeled
target data jointly. The parameters of source model and target model are softly
shared through a regularized log likelihood objective. An invertible projection
is employed to learn a new interlingual latent embedding space that compensates
for imperfect cross-lingual word embedding input. We evaluate our method on two
syntactic tasks: part-of-speech (POS) tagging and dependency parsing. On the
Universal Dependency Treebanks, we use English as the only source corpus and
transfer to a wide range of target languages. On the 10 languages in this
dataset that are distant from English, our method yields an average of 5.2%
absolute improvement on POS tagging and 8.3% absolute improvement on dependency
parsing over a direct transfer method using state-of-the-art discriminative
models.
