  Chinese parsing has traditionally been solved by three pipeline systems
including word-segmentation, part-of-speech tagging and dependency parsing
modules. In this paper, we propose an end-to-end Chinese parsing model based on
character inputs which jointly learns to output word segmentation,
part-of-speech tags and dependency structures. In particular, our parsing model
relies on word-char graph attention networks, which can enrich the character
inputs with external word knowledge. Experiments on three Chinese parsing
benchmark datasets show the effectiveness of our models, achieving the
state-of-the-art results on end-to-end Chinese parsing.
