You Can Do Better! If You Elaborate the Reason When Making Prediction

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.