  Unsupervised question answering is a promising yet challenging task, which
alleviates the burden of building large-scale annotated data in a new domain.
It motivates us to study the unsupervised multiple-choice question answering
(MCQA) problem. In this paper, we propose a novel framework designed to
generate synthetic MCQA data barely based on contexts from the universal domain
without relying on any form of manual annotation. Possible answers are
extracted and used to produce related questions, then we leverage both named
entities (NE) and knowledge graphs to discover plausible distractors to form
complete synthetic samples. Experiments on multiple MCQA datasets demonstrate
the effectiveness of our method.
