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Existing metrics for assessing question generation not only require costly human reference but also fail to take into account the input context of generation, rendering the lack of deep understanding of the relevance between the generated questions and input contexts.
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Neural question generation from text: A preliminary study
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Transformers: State-of-the-art natural language processing
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Learning to ask questions in open-domain conversational systems with typed decoders
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
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Learning to generate questions by learningwhat not to generate
Bang Liu, Mingjun Zhao, Di Niu, Kunfeng Lai, Yancheng He, Haojie Wei, and Yu Xu. 2019 · 2019
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How does bert answer questions? a layer-wise analysis of transformer representations
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Wenhao Yu, Lingfei Wu, Yu Deng, Ruchi Mahindru, Qingkai Zeng, Sinem Guven, and Meng Jiang. 2020b · 2020
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Asking questions the human way: Scalable question-answer generation from text corpus
Bang Liu, Haojie Wei, Di Niu, Haolan Chen, and Yancheng He. 2020 · 2043
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Are factuality checkers reliable? adversarial meta-evaluation of factuality in summarization
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