  Neural predictive models have achieved groundbreaking performance
improvements in various natural language processing tasks. However, most of
neural predictive models suffer from the lack of explainability of predictions,
limiting their practical utility, especially in the medical domain. This paper
proposes a novel neural predictive framework coupled with large pre-trained
language models to make a prediction and generate its corresponding explanation
simultaneously. We conducted a preliminary empirical 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 explanation text. 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.
