
# Dataset Card for COmmonsense Dataset Adversarially-authored by Humans

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## 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](https://github.com/patil-suraj) for adding this dataset.
task_categories:question-answering task_ids:multiple-choice-qa annotations_creators:crowdsourced language_creators:crowdsourced multilinguality:monolingual size_categories:1K<n<10K source_datasets:original language:en license:odc-by arxiv:1904.04365 region:us
{"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": {"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": 344900, "num_examples": 1665}, {"name": "validation", "num_bytes": 114199, "num_examples": 556}, {"name": "test", "num_bytes": 112097, "num_examples": 555}], "download_size": 379179, "dataset_size": 571196}, {"config_name": "fold_1", "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": 340978, "num_examples": 1665}, {"name": "validation", "num_bytes": 114199, "num_examples": 556}, {"name": "test", "num_bytes": 116019, "num_examples": 555}], "download_size": 379728, "dataset_size": 571196}, {"config_name": "fold_2", "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": 342281, "num_examples": 1665}, {"name": "validation", "num_bytes": 114199, "num_examples": 556}, {"name": "test", "num_bytes": 114716, "num_examples": 555}], "download_size": 379126, "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", "sequence": "string"}, {"name": "correct_answer_idx", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 342832, "num_examples": 1665}, {"name": "validation", "num_bytes": 114199, "num_examples": 556}, {"name": "test", "num_bytes": 114165, "num_examples": 555}], "download_size": 379178, "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"}], "splits": [{"name": "train", "num_bytes": 342832, "num_examples": 1665}, {"name": "validation", "num_bytes": 114165, "num_examples": 555}, {"name": "test", "num_bytes": 114199, "num_examples": 556}], "download_size": 379178, "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-*"}]}]}
TAGS
#task_categories-question-answering #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-odc-by #arxiv-1904.04365 #region-us 


# 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
  - 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: [Add homepage URL here if available (unless it's a GitHub repository)]()
- Repository: URL
- Paper: URL
- Paper: URL

### 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



### 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

The CODAH dataset is made available under the Open Data Commons Attribution License: URL





### Contributions

Thanks to @patil-suraj for adding this dataset.
# 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
  - 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: [Add homepage URL here if available (unless it's a GitHub repository)]()
- Repository: URL
- Paper: URL
- Paper: URL ### 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 ### 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

The CODAH dataset is made available under the Open Data Commons Attribution License: URL ### Contributions

Thanks to @patil-suraj for adding this dataset.
TAGS
#task_categories-question-answering #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-odc-by #arxiv-1904.04365 #region-us 
 # 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
  - 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: [Add homepage URL here if available (unless it's a GitHub repository)]()
- Repository: URL
- Paper: URL
- Paper: URL ### 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 ### 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

The CODAH dataset is made available under the Open Data Commons Attribution License: URL ### Contributions

Thanks to @patil-suraj for adding this dataset.
passage: TAGS
#task_categories-question-answering #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-odc-by #arxiv-1904.04365 #region-us 
# 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
  - 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: [Add homepage URL here if available (unless it's a GitHub repository)]()
- Repository: URL
- Paper: URL
- Paper: URL### 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### 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