  Building explainable systems is a critical problem in the field of Natural
Language Processing (NLP), since most machine learning models provide no
explanations for the predictions. Existing approaches for explainable machine
learning systems tend to focus on interpreting the outputs or the connections
between inputs and outputs. However, the fine-grained information is often
ignored, and the systems do not explicitly generate the human-readable
explanations. To better alleviate this problem, we propose a novel generative
explanation framework that learns to make classification decisions and generate
fine-grained explanations at the same time. More specifically, we introduce the
explainable factor and the minimum risk training approach that learn to
generate more reasonable explanations. We construct two new datasets that
contain summaries, rating scores, and fine-grained reasons. We conduct
experiments on both datasets, comparing with several strong neural network
baseline systems. Experimental results show that our method surpasses all
baselines on both datasets, and is able to generate concise explanations at the
same time.
