  When answering a question, people often draw upon their rich world knowledge
in addition to the particular context. While recent works retrieve supporting
facts/evidence from commonsense knowledge bases to supply additional
information to each question, there is still ample opportunity to advance it on
the quality of the evidence. It is crucial since the quality of the evidence is
the key to answering commonsense questions, and even determines the upper bound
on the QA systems performance. In this paper, we propose a recursive erasure
memory network (REM-Net) to cope with the quality improvement of evidence. To
address this, REM-Net is equipped with a module to refine the evidence by
recursively erasing the low-quality evidence that does not explain the question
answering. Besides, instead of retrieving evidence from existing knowledge
bases, REM-Net leverages a pre-trained generative model to generate candidate
evidence customized for the question. We conduct experiments on two commonsense
question answering datasets, WIQA and CosmosQA. The results demonstrate the
performance of REM-Net and show that the refined evidence is explainable.
