In this thesis, I explored several neural network-based models to resolve the issues of zero pronoun in Chinese English translation tasks. I reviewed previous work that attempts to take the resolution as a classification task, such as determining if a candidate in a given set is the antecedent of a zero pronoun, which can be categorized as rule-based and supervised methods. Existing methods either did not take the relationship between potential zero pronoun candidates into consideration or did not fully utilize attention to zero pronoun representations. In my experiments, I investigated attention-based neural network models as well as its application in reinforcement learning environment building on an existing neural model. In particular, I integrated an LSTM-tree-based module into the attention network, which encodes syntax information for zero pronoun resolution tasks. In addition, I apply Bi-Attention layers between modules to interactively learn the syntax and semantic alignment. Furthermore, I leveraged a reinforcement learning framework to fine-tune the proposed model, and experiment with different encoding strategies, i.e., FastText, BERT, and trained RNN-based embedding. I found that attention-based model with LSTM-tree- based module, fine-tuned under reinforcement learning framework that utilized FastText embedding achieves the best performance, superior to the baseline models. I evaluated the model performance on different categories of resources, of which FastText shows great potential in encoding web blog text.