This paper investigates the problem of dependency parsing with partially labeled data.
we focus on the realistic scenario where we have a small-scale training dataset with FA and a largescale training dataset with PA. We experiment with three settings for simulating PA and compare several directly-train and complete-then-train approaches with five mainstream parsers, i.e., Biaffine, LLGPar, LGPar, GN3Par and LTPar.