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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

Liangming Pan , Wenqiang Lei , Tat-Seng Chua , Min-Yen Kan This is my paper · ORCID
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 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  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.

  2. Can LLMs Ask Good Questions?

    cs.CL 2025-01 conditional novelty 5.0 of 10

    LLM-generated questions mostly ask for descriptions and long answers, and they use the source text more evenly than human-authored questions.

  3. DragonVerseQA: Open-Domain Long-Form Context-Aware Question-Answering

    cs.CL 2024-12 reject novelty 4.0 of 10

    DragonVerseQA is a 3,200-pair question-answering dataset for House of the Dragon and Game of Thrones episodes, built from summaries, reviews, and wiki data to support long-form narrative QA.

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