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.

中文翻译：

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OneStop QAMaker：以一站式方法从文本中提取问题答案对

大规模问答（QA）对对推进机器阅读理解和问答等研究领域至关重要。要从文档中构建质量检查对，需要确定如何提出问题以及相应的答案是什么。用于生成质量检查对的现有方法通常遵循流水线方法。即，他们首先选择最可能的候选答案范围，然后生成特定于答案的问题。但是，这种流水线方法不希望从文档中挖掘最合适的QA对，因为它忽略了问题生成和答案提取之间的联系，这可能导致QA对生成不兼容，即所选答案范围不适用于问题生成。但是，对于人类注释者，我们会考虑整个质量检查对，并考虑问题和答案之间的兼容性。受这种动机的启发，我们提出了一种名为OneStop的模型，该模型以一站式方法从文档生成质量检查对，而不是使用传统的管道方法。具体来说，问题及其对应的答案范围是同时提取的，问题的生成和答案提取的过程相互影响。此外，由于OneStop仅涉及解决复杂QA生成任务的一种模型，因此在工业场景中进行培训和部署的效率要高得多。我们对三个大型机器阅读理解数据集进行了全面的实验：SQuAD，NewsQA和DuReader。