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Interpreting the predictions of existing Question Answering (QA) models iscritical to many real-world intelligent applications, such as QA systems forhealthcare, education, and finance. However, existing QA models lackinterpretability and provide no feedback or explanation for end-users to helpthem understand why a specific prediction is the answer to a question. In thisresearch, we argue that the evidences of an answer is critical to enhancing theinterpretability of QA models. Unlike previous research that simply extractsseveral sentence(s) in the context as evidence, we are the first to explicitlydefine the concept of evidence as the supporting facts in a context which areinformative, concise, and readable. Besides, we provide effective strategies toquantitatively measure the informativeness, conciseness and readability ofevidence. Furthermore, we propose Grow-and-Clip Evidence Distillation (GCED)algorithm to extract evidences from the contexts by trade-off informativeness,conciseness, and readability. We conduct extensive experiments on the SQuAD andTriviaQA datasets with several baseline models to evaluate the effect of GCEDon interpreting answers to questions. Human evaluation are also carried out tocheck the quality of distilled evidences. Experimental results show thatautomatic distilled evidences have human-like informativeness, conciseness andreadability, which can enhance the interpretability of the answers toquestions.

Grow-and-Clip: Informative-yet-Concise Evidence Distillation for Answer Explanation