• Description:

Story Cloze Test is a new commonsense reasoning framework for evaluating story understanding, story generation, and script learning. This test requires a system to choose the correct ending to a four-sentence story.

  • Additional Documentation: Explore on Papers With Code

  • Config description: 2018 year

  • Homepage:

  • Source code: tfds.datasets.story_cloze.Builder

  • Versions:

    • 1.0.0 (default): Initial release.
  • Download size: Unknown size

  • Manual download instructions: This dataset requires you to download the source data manually into download_config.manual_dir (defaults to ~/tensorflow_datasets/downloads/manual/):
    Visit and fill out the google form to obtain the datasets. You will receive an email with the link to download the datasets. For the 2016 data, the validation and test file needs to be renamed to cloze_testval_spring2016.csv and cloze_testtest_spring2016.csv respectively. For 2018 version, the validation and test file needs to be renamed to cloze_testval_winter2018.csv and to cloze_testtest_winter2018.csv respectively. Move both these files to the manual directory.

  • Auto-cached (documentation): Yes

  • Feature structure:

    'context': Text(shape=(), dtype=string),
    'endings': Sequence(Text(shape=(), dtype=string)),
    'label': int32,
  • Feature documentation:
Feature Class Shape Dtype Description
context Text string
endings Sequence(Text) (None,) string
label Tensor int32
    title = "Tackling the Story Ending Biases in The Story Cloze Test",
    author = "Sharma, Rishi  and
      Allen, James  and
      Bakhshandeh, Omid  and
      Mostafazadeh, Nasrin",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "",
    doi = "10.18653/v1/P18-2119",
    pages = "752--757",
    abstract = "The Story Cloze Test (SCT) is a recent framework for evaluating story comprehension and script learning. There have been a variety of models tackling the SCT so far. Although the original goal behind the SCT was to require systems to perform deep language understanding and commonsense reasoning for successful narrative understanding, some recent models could perform significantly better than the initial baselines by leveraging human-authorship biases discovered in the SCT dataset. In order to shed some light on this issue, we have performed various data analysis and analyzed a variety of top performing models presented for this task. Given the statistics we have aggregated, we have designed a new crowdsourcing scheme that creates a new SCT dataset, which overcomes some of the biases. We benchmark a few models on the new dataset and show that the top-performing model on the original SCT dataset fails to keep up its performance. Our findings further signify the importance of benchmarking NLP systems on various evolving test sets.",

story_cloze/2016 (default config)

  • Dataset size: 1.15 MiB

  • Splits:

Split Examples
'test' 1,871
'validation' 1,871


  • Dataset size: 1015.04 KiB

  • Splits:

Split Examples
'test' 1,571
'validation' 1,571