  While part-of-speech (POS) tagging and dependency parsing are observed to be
closely related, existing work on joint modeling with manually crafted feature
templates suffers from the feature sparsity and incompleteness problems. In
this paper, we propose an approach to joint POS tagging and dependency parsing
using transition-based neural networks. Three neural network based classifiers
are designed to resolve shift/reduce, tagging, and labeling conflicts.
Experiments show that our approach significantly outperforms previous methods
for joint POS tagging and dependency parsing across a variety of natural
languages.
