  Neural predictive models have achieved remarkable performance improvements in
various natural language processing tasks. However, most neural predictive
models suffer from the lack of explainability of predictions, limiting their
practical utility. This paper proposes a neural predictive approach to make a
prediction and generate its corresponding explanation simultaneously. It
leverages the knowledge entailed in explanations as an additional distillation
signal for more efficient learning. We conduct a preliminary study on Chinese
medical multiple-choice question answering, English natural language inference,
and commonsense question answering tasks. The experimental results show that
the proposed approach can generate reasonable explanations for its predictions
even with a small-scale training corpus. The proposed method also achieves
improved prediction accuracy on three datasets, which indicates that making
predictions can benefit from generating the explanation in the decision
process.
