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Recent Advances in Neural Question Generation

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arxiv 1905.08949 v3 pith:FIAOA3HY submitted 2019-05-22 cs.CL

classification cs.CL
keywords generationneuralquestionemerginglevelsadvancesaheadbellwether
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Emerging research in Neural Question Generation (NQG) has started to integrate a larger variety of inputs, and generating questions requiring higher levels of cognition. These trends point to NQG as a bellwether for NLP, about how human intelligence embodies the skills of curiosity and integration. We present a comprehensive survey of neural question generation, examining the corpora, methodologies, and evaluation methods. From this, we elaborate on what we see as emerging on NQG's trend: in terms of the learning paradigms, input modalities, and cognitive levels considered by NQG. We end by pointing out the potential directions ahead.

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Cited by 1 Pith paper

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  1. Evaluating the Evaluation of Diversity in Commonsense Generation

    cs.CL 2025-05 conditional novelty 6.0 of 10

    Content-based diversity metrics, such as Vendi Score and Chamfer distance, agree with LLM-based diversity ratings far better than form-based metrics like self-BLEU across three commonsense generation datasets.

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