  In this paper, we propose efficient and less resource-intensive strategies
for parsing of code-mixed data. These strategies are not constrained by
in-domain annotations, rather they leverage pre-existing monolingual annotated
resources for training. We show that these methods can produce significantly
better results as compared to an informed baseline. Besides, we also present a
data set of 450 Hindi and English code-mixed tweets of Hindi multilingual
speakers for evaluation. The data set is manually annotated with Universal
Dependencies.
