  Open-domain Question Answering (ODQA) has achieved significant results in
terms of supervised learning manner. However, data annotation cannot also be
irresistible for its huge demand in an open domain. Though unsupervised QA or
unsupervised Machine Reading Comprehension (MRC) has been tried more or less,
unsupervised ODQA has not been touched according to our best knowledge. This
paper thus pioneers the work of unsupervised ODQA by formally introducing the
task and proposing a series of key data construction methods. Our exploration
in this work inspiringly shows unsupervised ODQA can reach up to 86%
performance of supervised ones.
