Deep Reinforcement Learning for Chinese Zero Pronoun Resolution
Qingyu Yin, Zhang Yu, Weinan Zhang, Ting Liu, William Yang Wang · 2018
Deep neural network models for Chinese zero pronoun resolution learn semantic information for zero pronoun and candidate antecedents, but tend to be short-sightedthey 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.