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        <title>An Empirical Comparison of LM-based Question and Answer Generation Methods</title>
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        <namePart type="given">Asahi</namePart>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-acl.899</identifier>
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%0 Conference Proceedings
%T An Empirical Comparison of LM-based Question and Answer Generation Methods
%A Ushio, Asahi
%A Alva-Manchego, Fernando
%A Camacho-Collados, Jose
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F ushio-etal-2023-empirical
%X 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.
%R 10.18653/v1/2023.findings-acl.899
%U https://aclanthology.org/2023.findings-acl.899
%U https://doi.org/10.18653/v1/2023.findings-acl.899
%P 14262-14272

Markdown (Informal)

[An Empirical Comparison of LM-based Question and Answer Generation Methods](https://aclanthology.org/2023.findings-acl.899) (Ushio et al., Findings 2023)

• An Empirical Comparison of LM-based Question and Answer Generation Methods (Ushio et al., Findings 2023)

ACL

• Asahi Ushio, Fernando Alva-Manchego, and Jose Camacho-Collados. 2023. An Empirical Comparison of LM-based Question and Answer Generation Methods. In Findings of the Association for Computational Linguistics: ACL 2023, pages 14262–14272, Toronto, Canada. Association for Computational Linguistics.