Neural models are now being widely applied in natural language processing and yield excellent results on a wide range of text analysis tasks. At the same time, because they do not make use of explicit features derived from text, the models suffer from a lack of interpretability, i.e., often times the decision the the model makes on individual instances is hard to explain. A number of methods for increasing the interpretability of neural model has been proposed in the literature, such as representation erasure and gradient-based method. The topic of this thesis are the methods for interpreting neural models applied to natural language processing tasks. Do a literature survey on methods for interpreting the decisions of neural models, with an emphasis on methods that are applicable to natural language processing. Implement or adopt existing implementations of neural models for a few typical natural language processing tasks, such as sentiment analysis, question answering and natural language inference. Chose one or two standard methods for interpreting neural models, such as representation erasure and gradient-based methods, and apply them to these models and tasks. Do a comparison and a systematic analysis of the methods across different models and tasks. All references must be cited, and all source code, documentation, executables, and datasets must be provided with the thesis.