Question and answer generation (QAG) consists of generating a set of
question-answer pairs given a context (e.g. a paragraph). This task has a
variety of applications, such as data augmentation for question answering (QA)
models, information retrieval and education. In this paper, we establish
baselines with three different QAG methodologies that leverage
sequence-to-sequence language model (LM) fine-tuning. Experiments show that an
end-to-end QAG model, which is computationally light at both training and
inference times, is generally robust and outperforms other more convoluted
approaches. However, there are differences depending on the underlying
generative LM. Finally, our analysis shows that QA models fine-tuned solely on
generated question-answer pairs can be competitive when compared to supervised
QA models trained on human-labeled data.

本文旨在建立三种不同的 QAG 方法的基线，利用序列到序列语言模型进行微调。实验证明，一种端到端的 QAG 模型，它在训练和推理时计算量都很轻，通常是稳健的，并且胜过其他复杂的方法。然而，这取决于底层生成模型的不同。最后，我们的分析表明，仅基于生成的问答对微调的 QA 模型可以与人类标注数据上训练的监督 QA 模型具有竞争力。