  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.
