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.
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.
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groundbreaking
remarkable
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of
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, especially in the medical domain
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novel neural predictive framework coupled with large pre-trained language models
neural predictive approach
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We conducted a preliminary empirical
It leverages the knowledge entailed in explanations as an additional distillation signal for more efficient learning. We conduct a preliminary
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,
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explanation text
corpus