  Existing knowledge-enhanced methods have achieved remarkable results in
certain QA tasks via obtaining diverse knowledge from different knowledge
bases. However, limited by the properties of retrieved knowledge, they still
have trouble benefiting from both the knowledge relevance and distinguishment
simultaneously. To address the challenge, we propose CPACE, a Concept-centric
Prompt-bAsed Contrastive Explanation Generation model, which aims to convert
obtained symbolic knowledge into a contrastive explanation for better
distinguishing the differences among given candidates. Firstly, following
previous works, we retrieve different types of symbolic knowledge with a
concept-centric knowledge extraction module. After that, we generate
corresponding contrastive explanations using acquired symbolic knowledge and
explanation prompts as guidance for better modeling the knowledge
distinguishment and interpretability. Finally, we regard the generated
contrastive explanation as external knowledge for downstream task enhancement.
We conduct a series of experiments on three widely-used question-answering
datasets: CSQA, QASC, and OBQA. Experimental results demonstrate that with the
help of generated contrastive explanation, our CPACE model achieves new SOTA on
CSQA (89.8% on the testing set, 0.9% higher than human performance), and gains
impressive improvement on QASC and OBQA (4.2% and 3.5%, respectively).
