Dataset: cos_e


Dataset Card for "cos_e"

Table of Contents

Dataset Description

Dataset Summary

Common Sense Explanations (CoS-E) allows for training language models to automatically generate explanations that can be used during training and inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework.

Supported Tasks

More Information Needed

Languages

More Information Needed

Dataset Structure

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

Data Instances

v1.0

  • Size of downloaded dataset files: 4.10 MB
  • Size of the generated dataset: 2.23 MB
  • Total amount of disk used: 6.33 MB

An example of 'train' looks as follows.

{
    "abstractive_explanation": "this is open-ended",
    "answer": "b",
    "choices": ["a", "b", "c"],
    "extractive_explanation": "this is selected train",
    "id": "42",
    "question": "question goes here."
}

v1.11

  • Size of downloaded dataset files: 6.23 MB
  • Size of the generated dataset: 2.91 MB
  • Total amount of disk used: 9.14 MB

An example of 'train' looks as follows.

{
    "abstractive_explanation": "this is open-ended",
    "answer": "b",
    "choices": ["a", "b", "c"],
    "extractive_explanation": "this is selected train",
    "id": "42",
    "question": "question goes here."
}

Data Fields

The data fields are the same among all splits.

v1.0

  • id: a string feature.
  • question: a string feature.
  • choices: a list of string features.
  • answer: a string feature.
  • abstractive_explanation: a string feature.
  • extractive_explanation: a string feature.

v1.11

  • id: a string feature.
  • question: a string feature.
  • choices: a list of string features.
  • answer: a string feature.
  • abstractive_explanation: a string feature.
  • extractive_explanation: a string feature.

Data Splits Sample Size

name train validation
v1.0 7610 950
v1.11 9741 1221

Dataset Creation

Curation Rationale

More Information Needed

Source Data

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Annotations

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

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Licensing Information

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Citation Information


@inproceedings{rajani2019explain,
     title = "Explain Yourself! Leveraging Language models for Commonsense Reasoning",
    author = "Rajani, Nazneen Fatema  and
      McCann, Bryan  and
      Xiong, Caiming  and
      Socher, Richard",
      year="2019",
    booktitle = "Proceedings of the 2019 Conference of the Association for Computational Linguistics (ACL2019)",
    url ="https://arxiv.org/abs/1906.02361"
}

Models trained or fine-tuned on cos_e

None yet