  Large-scale question-answer (QA) pairs are critical for advancing research
areas like machine reading comprehension and question answering. To construct
QA pairs from documents requires determining how to ask a question and what is
the corresponding answer. Existing methods for QA pair generation usually
follow a pipeline approach. Namely, they first choose the most likely candidate
answer span and then generate the answer-specific question. This pipeline
approach, however, is undesired in mining the most appropriate QA pairs from
documents since it ignores the connection between question generation and
answer extraction, which may lead to incompatible QA pair generation, i.e., the
selected answer span is inappropriate for question generation. However, for
human annotators, we take the whole QA pair into account and consider the
compatibility between question and answer. Inspired by such motivation, instead
of the conventional pipeline approach, we propose a model named OneStop
generate QA pairs from documents in a one-stop approach. Specifically,
questions and their corresponding answer span is extracted simultaneously and
the process of question generation and answer extraction mutually affect each
other. Additionally, OneStop is much more efficient to be trained and deployed
in industrial scenarios since it involves only one model to solve the complex
QA generation task. We conduct comprehensive experiments on three large-scale
machine reading comprehension datasets: SQuAD, NewsQA, and DuReader. The
experimental results demonstrate that our OneStop model outperforms the
baselines significantly regarding the quality of generated questions, quality
of generated question-answer pairs, and model efficiency.
