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Dataset Card for "common_gen"

Dataset Summary

CommonGen is a constrained text generation task, associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning. Given a set of common concepts; the task is to generate a coherent sentence describing an everyday scenario using these concepts.

CommonGen is challenging because it inherently requires 1) relational reasoning using background commonsense knowledge, and 2) compositional generalization ability to work on unseen concept combinations. Our dataset, constructed through a combination of crowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and 50k sentences in total.

Supported Tasks and Leaderboards

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Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances


  • Size of downloaded dataset files: 1.76 MB
  • Size of the generated dataset: 6.88 MB
  • Total amount of disk used: 8.64 MB

An example of 'train' looks as follows.

    "concept_set_idx": 0,
    "concepts": ["ski", "mountain", "skier"],
    "target": "Three skiers are skiing on a snowy mountain."

Data Fields

The data fields are the same among all splits.


  • concept_set_idx: a int32 feature.
  • concepts: a list of string features.
  • target: a string feature.

Data Splits

name train validation test
default 67389 4018 1497

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

    title = "{C}ommon{G}en: A Constrained Text Generation Challenge for Generative Commonsense Reasoning",
    author = "Lin, Bill Yuchen  and
      Zhou, Wangchunshu  and
      Shen, Ming  and
      Zhou, Pei  and
      Bhagavatula, Chandra  and
      Choi, Yejin  and
      Ren, Xiang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "",
    doi = "10.18653/v1/2020.findings-emnlp.165",
    pages = "1823--1840"


Thanks to @JetRunner, @yuchenlin, @thomwolf, @lhoestq for adding this dataset.

Models trained or fine-tuned on common_gen