  Deep neural network models for Chinese zero pronoun resolution learn semantic
information for zero pronoun and candidate antecedents, but tend to be
short-sighted---they often make local decisions. They typically predict
coreference chains between the zero pronoun and one single candidate antecedent
one link at a time, while overlooking their long-term influence on future
decisions. Ideally, modeling useful information of preceding potential
antecedents is critical when later predicting zero pronoun-candidate antecedent
pairs. In this study, we show how to integrate local and global decision-making
by exploiting deep reinforcement learning models. With the help of the
reinforcement learning agent, our model learns the policy of selecting
antecedents in a sequential manner, where useful information provided by
earlier predicted antecedents could be utilized for making later coreference
decisions. Experimental results on OntoNotes 5.0 dataset show that our
technique surpasses the state-of-the-art models.
