README.md

metadata

languages:
  - en
paperswithcode_id: cos-e
pretty_name: Commonsense Explanations Dataset

Dataset Card for "cos_e"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://github.com/salesforce/cos-e
• Repository: More Information Needed
• Paper: More Information Needed
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 10.33 MB
• Size of the generated dataset: 5.14 MB
• Total amount of disk used: 15.47 MB

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 and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

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

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

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

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

More Information Needed

Licensing Information

More Information Needed

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"
}

Contributions

Thanks to @lewtun, @thomwolf, @mariamabarham, @patrickvonplaten, @albertvillanova, @lhoestq for adding this dataset.