OneStop QAMaker：一站式从文本中提取问答对
OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach
摘要：
大规模问答（QA）对对于推进机器阅读理解和问答等研究领域至关重要。从文档中构建QA对需要确定如何提出问题以及相应的答案是什么。现有的QA对生成方法通常遵循流水线方法。也就是说，他们首先选择最有可能的候选答案范围，然后生成答案特定的问题。然而，这种管道方法在从文档中挖掘最合适的QA对时是不期望的，因为它忽略了问题生成和答案提取之间的联系，这可能导致不兼容的QA对生成，即，所选择的答案范围不适合问题生成。然而，对于人类注释者，我们考虑整个QA对，并考虑问题和答案之间的兼容性。受这种动机的启发，而不是传统的管道方法，我们提出了一个名为OneStop生成QA对从文档中的一站式方法的模型。具体地说，问题及其对应的答案跨度是同时提取的，问题生成和答案提取的过程相互影响。此外，OneStop在工业场景中的训练和部署效率要高得多，因为它只涉及一个模型来解决复杂的QA生成任务。我们在SQuAD、NewsQA和DuReader三个大规模机器阅读理解数据集上进行了综合实验。实验结果表明，我们的OneStop模型在生成问题的质量，生成的问答对的质量和模型效率方面显着优于基线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
