An Empirical Comparison of LM-based Question and Answer Generation Methods
This work addresses data augmentation for question answering models, but it is incremental as it compares existing methods without introducing new paradigms.
The paper tackled the problem of generating question-answer pairs from text, finding that an end-to-end language model approach is robust and outperforms more complex methods, with QA models trained on generated data being competitive against those using human-labeled data.
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.