The novel idea behind our hybrid generative-discriminative method is to explicitly capture the fine-grained information inferred from raw texts, utilizing the information to help interpret the predicted classification results and improve the overall performance.
we introduce the notion of an explainable factor and a minimum risk training method that learn to generate reasonable explanations for the overall predict results.