  Dependency parsing is a longstanding natural language processing task, with
its outputs crucial to various downstream tasks. Recently, neural network based
(NN-based) dependency parsing has achieved significant progress and obtained
the state-of-the-art results. As we all know, NN-based approaches require
massive amounts of labeled training data, which is very expensive because it
requires human annotation by experts. Thus few industrial-oriented dependency
parser tools are publicly available. In this report, we present Baidu
Dependency Parser (DDParser), a new Chinese dependency parser trained on a
large-scale manually labeled dataset called Baidu Chinese Treebank (DuCTB).
DuCTB consists of about one million annotated sentences from multiple sources
including search logs, Chinese newswire, various forum discourses, and
conversation programs. DDParser is extended on the graph-based biaffine parser
to accommodate to the characteristics of Chinese dataset. We conduct
experiments on two test sets: the standard test set with the same distribution
as the training set and the random test set sampled from other sources, and the
labeled attachment scores (LAS) of them are 92.9% and 86.9% respectively.
DDParser achieves the state-of-the-art results, and is released at
https://github.com/baidu/DDParser.
