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squad_question_generation

  • Description:

Question generation using squad dataset using data splits described in 'Neural Question Generation from Text: A Preliminary Study' (Zhou et al, 2017) and 'Learning to Ask: Neural Question Generation for Reading Comprehension' (Du et al, 2017).

@inproceedings{du-etal-2017-learning,
    title = "Learning to Ask: Neural Question Generation for Reading Comprehension",
    author = "Du, Xinya  and Shao, Junru  and Cardie, Claire",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P17-1123",
    doi = "10.18653/v1/P17-1123",
    pages = "1342--1352",
}

@inproceedings{rajpurkar-etal-2016-squad,
    title = "{SQ}u{AD}: 100,000+ Questions for Machine Comprehension of Text",
    author = "Rajpurkar, Pranav  and Zhang, Jian  and Lopyrev, Konstantin  and Liang, Percy",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D16-1264",
    doi = "10.18653/v1/D16-1264",
    pages = "2383--2392",
}

squad_question_generation/split_du (default config)

  • Config description: Answer independent question generation from passage-level contexts (Du et al, 2017).

  • Download size: 62.83 MiB

  • Dataset size: 84.67 MiB

  • Splits:

Split Examples
'test' 11,877
'train' 75,722
'validation' 10,570
  • Feature structure:
FeaturesDict({
    'answer': Text(shape=(), dtype=object),
    'context_passage': Text(shape=(), dtype=object),
    'question': Text(shape=(), dtype=object),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
answer Text object
context_passage Text object
question Text object

squad_question_generation/split_zhou

  • Config description: Answer-span dependent question generation from sentence- and passage-level contexts (Zhou et al, 2017).

  • Download size: 62.52 MiB

  • Dataset size: 111.02 MiB

  • Splits:

Split Examples
'test' 8,964
'train' 86,635
'validation' 8,965
  • Feature structure:
FeaturesDict({
    'answer': Text(shape=(), dtype=object),
    'context_passage': Text(shape=(), dtype=object),
    'context_sentence': Text(shape=(), dtype=object),
    'question': Text(shape=(), dtype=object),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
answer Text object
context_passage Text object
context_sentence Text object
question Text object