  In the training of transition-based dependency parsers, an oracle is used to
predict a transition sequence for a sentence and its gold tree. However, the
transition system may exhibit ambiguity, that is, there can be multiple correct
transition sequences that form the gold tree. We propose to make use of the
property in the training of neural dependency parsers, and present the Hybrid
Oracle. The new oracle gives all the correct transitions for a parsing state,
which are used in the cross entropy loss function to provide better supervisory
signal. It is also used to generate different transition sequences for a
sentence to better explore the training data and improve the generalization
ability of the parser. Evaluations show that the parsers trained using the
hybrid oracle outperform the parsers using the traditional oracle in Chinese
dependency parsing. We provide analysis from a linguistic view. The code is
available at https://github.com/lancopku/nndep .
