metadata

annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
language:
  - en
license: odc-by
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - question-answering
task_ids:
  - multiple-choice-qa
paperswithcode_id: codah
pretty_name: COmmonsense Dataset Adversarially-authored by Humans
dataset_info:
  - config_name: codah
    features:
      - name: id
        dtype: int32
      - name: question_category
        dtype:
          class_label:
            names:
              '0': Idioms
              '1': Reference
              '2': Polysemy
              '3': Negation
              '4': Quantitative
              '5': Others
      - name: question_propmt
        dtype: string
      - name: candidate_answers
        sequence: string
      - name: correct_answer_idx
        dtype: int32
    splits:
      - name: train
        num_bytes: 571196
        num_examples: 2776
    download_size: 352902
    dataset_size: 571196
  - config_name: fold_0
    features:
      - name: id
        dtype: int32
      - name: question_category
        dtype:
          class_label:
            names:
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              '1': Reference
              '2': Polysemy
              '3': Negation
              '4': Quantitative
              '5': Others
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        dtype: string
      - name: candidate_answers
        sequence: string
      - name: correct_answer_idx
        dtype: int32
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        num_examples: 1665
      - name: validation
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        num_examples: 556
      - name: test
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        num_examples: 555
    download_size: 379179
    dataset_size: 571196
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    features:
      - name: id
        dtype: int32
      - name: question_category
        dtype:
          class_label:
            names:
              '0': Idioms
              '1': Reference
              '2': Polysemy
              '3': Negation
              '4': Quantitative
              '5': Others
      - name: question_propmt
        dtype: string
      - name: candidate_answers
        sequence: string
      - name: correct_answer_idx
        dtype: int32
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        num_examples: 1665
      - name: validation
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        num_examples: 556
      - name: test
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    dataset_size: 571196
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    features:
      - name: id
        dtype: int32
      - name: question_category
        dtype:
          class_label:
            names:
              '0': Idioms
              '1': Reference
              '2': Polysemy
              '3': Negation
              '4': Quantitative
              '5': Others
      - name: question_propmt
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      - name: candidate_answers
        sequence: string
      - name: correct_answer_idx
        dtype: int32
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        num_examples: 1665
      - name: validation
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        num_examples: 556
      - name: test
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    dataset_size: 571196
  - config_name: fold_3
    features:
      - name: id
        dtype: int32
      - name: question_category
        dtype:
          class_label:
            names:
              '0': Idioms
              '1': Reference
              '2': Polysemy
              '3': Negation
              '4': Quantitative
              '5': Others
      - name: question_propmt
        dtype: string
      - name: candidate_answers
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      - name: correct_answer_idx
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      - name: validation
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      - name: test
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    dataset_size: 571196
  - config_name: fold_4
    features:
      - name: id
        dtype: int32
      - name: question_category
        dtype:
          class_label:
            names:
              '0': Idioms
              '1': Reference
              '2': Polysemy
              '3': Negation
              '4': Quantitative
              '5': Others
      - name: question_propmt
        dtype: string
      - name: candidate_answers
        sequence: string
      - name: correct_answer_idx
        dtype: int32
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      - name: validation
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      - name: test
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        num_examples: 556
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    dataset_size: 571196
configs:
  - config_name: codah
    data_files:
      - split: train
        path: codah/train-*
  - config_name: fold_0
    data_files:
      - split: train
        path: fold_0/train-*
      - split: validation
        path: fold_0/validation-*
      - split: test
        path: fold_0/test-*
  - config_name: fold_1
    data_files:
      - split: train
        path: fold_1/train-*
      - split: validation
        path: fold_1/validation-*
      - split: test
        path: fold_1/test-*
  - config_name: fold_2
    data_files:
      - split: train
        path: fold_2/train-*
      - split: validation
        path: fold_2/validation-*
      - split: test
        path: fold_2/test-*
  - config_name: fold_3
    data_files:
      - split: train
        path: fold_3/train-*
      - split: validation
        path: fold_3/validation-*
      - split: test
        path: fold_3/test-*
  - config_name: fold_4
    data_files:
      - split: train
        path: fold_4/train-*
      - split: validation
        path: fold_4/validation-*
      - split: test
        path: fold_4/test-*

Dataset Card for COmmonsense Dataset Adversarially-authored by Humans

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: Add homepage URL here if available (unless it's a GitHub repository)
Repository: https://github.com/Websail-NU/CODAH
Paper: https://aclanthology.org/W19-2008/
Paper: https://arxiv.org/abs/1904.04365

Dataset Summary

The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use this information to design challenging commonsense questions.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

[More Information Needed]

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

[More Information Needed]

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

[More Information Needed]

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

The CODAH dataset is made available under the Open Data Commons Attribution License: http://opendatacommons.org/licenses/by/1.0/

Citation Information

@inproceedings{chen-etal-2019-codah,
    title = "{CODAH}: An Adversarially-Authored Question Answering Dataset for Common Sense",
    author = "Chen, Michael  and
      D{'}Arcy, Mike  and
      Liu, Alisa  and
      Fernandez, Jared  and
      Downey, Doug",
    editor = "Rogers, Anna  and
      Drozd, Aleksandr  and
      Rumshisky, Anna  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W19-2008",
    doi = "10.18653/v1/W19-2008",
    pages = "63--69",
    abstract = "Commonsense reasoning is a critical AI capability, but it is difficult to construct challenging datasets that test common sense. Recent neural question answering systems, based on large pre-trained models of language, have already achieved near-human-level performance on commonsense knowledge benchmarks. These systems do not possess human-level common sense, but are able to exploit limitations of the datasets to achieve human-level scores. We introduce the CODAH dataset, an adversarially-constructed evaluation dataset for testing common sense. CODAH forms a challenging extension to the recently-proposed SWAG dataset, which tests commonsense knowledge using sentence-completion questions that describe situations observed in video. To produce a more difficult dataset, we introduce a novel procedure for question acquisition in which workers author questions designed to target weaknesses of state-of-the-art neural question answering systems. Workers are rewarded for submissions that models fail to answer correctly both before and after fine-tuning (in cross-validation). We create 2.8k questions via this procedure and evaluate the performance of multiple state-of-the-art question answering systems on our dataset. We observe a significant gap between human performance, which is 95.3{\%}, and the performance of the best baseline accuracy of 65.3{\%} by the OpenAI GPT model.",
}

Contributions

Thanks to @patil-suraj for adding this dataset.

Datasets:
codah

Dataset Card for COmmonsense Dataset Adversarially-authored by Humans

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

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation process

Who are the annotators?

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