During the past decades, due to the lack of sufficient labeled data, most studies on crossdomain parsing focus on unsupervised domain adaptation, assuming there is no targetdomain training data.However, unsupervised approaches make limited progress so far due to the intrinsic difficulty of both domain adaptation and parsing.This paper tackles the semi-supervised domain adaptation problem for Chinese dependency parsing, based on two newly-annotated large-scale domain-specific datasets.1 We propose a simple domain embedding approach to merge the sourceand target-domain training data, which is shown to be more effective than both direct corpus concatenation and multi-task learning.In order to utilize unlabeled target-domain data, we employ the recent contextualized word representations and show that a simple fine-tuning procedure can further boost cross-domain parsing accuracy by large margins.