
# Dataset Card for Wino_Bias dataset

## 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:** [WinoBias](https://uclanlp.github.io/corefBias/overview)
- **Repository:**
- **Paper:** [Arxiv](https://arxiv.org/abs/1804.06876)
- **Leaderboard:**
- **Point of Contact:**

### Dataset Summary

WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.
The corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter).

### Supported Tasks and Leaderboards

The underlying task is coreference resolution. 
### Languages

English

## Dataset Structure

### Data Instances

The dataset has 4 subsets: `type1_pro`, `type1_anti`, `type2_pro` and `type2_anti`.

The `*_pro` subsets contain sentences that reinforce gender stereotypes (e.g. mechanics are male, nurses are female), whereas the `*_anti` datasets contain "anti-stereotypical" sentences  (e.g. mechanics are female, nurses are male).

The `type1` (*WB-Knowledge*) subsets contain sentences for which world knowledge is necessary to resolve the co-references, and `type2` (*WB-Syntax*) subsets require only the syntactic information present in the sentence to resolve them.

### Data Fields

    - document_id = This is a variation on the document filename
    - part_number = Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
    - word_num = This is the word index of the word in that sentence.
    - tokens = This is the token as segmented/tokenized in the Treebank.
    - pos_tags = This is the Penn Treebank style part of speech. When parse information is missing, all part of speeches except the one for which there is some sense or proposition annotation   are marked with a XX tag. The verb is marked with just a VERB tag.
    - parse_bit = This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column. When the parse information is missing, the first word of a sentence is tagged as "(TOP*" and the last word is tagged as "*)" and all intermediate words are tagged with a "*".
    - predicate_lemma = The predicate lemma is mentioned for the rows for which we have semantic role information or word sense information. All other rows are marked with a "-".
    - predicate_framenet_id = This is the PropBank frameset ID of the predicate in predicate_lemma.
    - word_sense = This is the word sense of the word in Column tokens.
    - speaker = This is the speaker or author name where available.
    - ner_tags = These columns identifies the spans representing various named entities. For documents which do not have named entity annotation, each line is represented with an "*".
    - verbal_predicates = There is one column each of predicate argument structure information for the predicate mentioned in predicate_lemma. If there are no predicates tagged in a sentence this is a single column with all rows marked with an "*".

### Data Splits

Dev and Test Split available

## Dataset Creation

### Curation Rationale

The WinoBias dataset was introduced in 2018 (see [paper](https://arxiv.org/abs/1804.06876)), with its original task being *coreference resolution*, which is a task that aims to identify mentions that refer to the same entity or person.

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

 The dataset was created by researchers familiar with the WinoBias project, based on two prototypical templates provided by the authors, in which entities interact in plausible ways.

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

"Researchers familiar with the [WinoBias] project"

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[Recent work](https://www.microsoft.com/en-us/research/uploads/prod/2021/06/The_Salmon_paper.pdf) has shown that this dataset contains grammatical issues, incorrect or ambiguous labels, and stereotype conflation, among other limitations. 

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez and Kai-Wei Chan

### Licensing Information

MIT Licence

### Citation Information

@article{DBLP:journals/corr/abs-1804-06876,
  author    = {Jieyu Zhao and
               Tianlu Wang and
               Mark Yatskar and
               Vicente Ordonez and
               Kai{-}Wei Chang},
  title     = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods},
  journal   = {CoRR},
  volume    = {abs/1804.06876},
  year      = {2018},
  url       = {http://arxiv.org/abs/1804.06876},
  archivePrefix = {arXiv},
  eprint    = {1804.06876},
  timestamp = {Mon, 13 Aug 2018 16:47:01 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1804-06876.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

### Contributions

Thanks to [@akshayb7](https://github.com/akshayb7) for adding this dataset. Updated by [@JieyuZhao](https://github.com/JieyuZhao).
task_categories:token-classification task_ids:coreference-resolution annotations_creators:expert-generated language_creators:expert-generated multilinguality:monolingual size_categories:1K<n<10K source_datasets:original language:en license:mit arxiv:1804.06876 region:us
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TAGS
#task_categories-token-classification #task_ids-coreference-resolution #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-mit #arxiv-1804.06876 #region-us 


# Dataset Card for Wino_Bias dataset

## 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: WinoBias
- Repository:
- Paper: Arxiv
- Leaderboard:
- Point of Contact:

### Dataset Summary

WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.
The corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter).

### Supported Tasks and Leaderboards

The underlying task is coreference resolution. 
### Languages

English

## Dataset Structure

### Data Instances

The dataset has 4 subsets: 'type1_pro', 'type1_anti', 'type2_pro' and 'type2_anti'.

The '*_pro' subsets contain sentences that reinforce gender stereotypes (e.g. mechanics are male, nurses are female), whereas the '*_anti' datasets contain "anti-stereotypical" sentences  (e.g. mechanics are female, nurses are male).

The 'type1' (*WB-Knowledge*) subsets contain sentences for which world knowledge is necessary to resolve the co-references, and 'type2' (*WB-Syntax*) subsets require only the syntactic information present in the sentence to resolve them.

### Data Fields

    - document_id = This is a variation on the document filename
    - part_number = Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
    - word_num = This is the word index of the word in that sentence.
    - tokens = This is the token as segmented/tokenized in the Treebank.
    - pos_tags = This is the Penn Treebank style part of speech. When parse information is missing, all part of speeches except the one for which there is some sense or proposition annotation   are marked with a XX tag. The verb is marked with just a VERB tag.
    - parse_bit = This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column. When the parse information is missing, the first word of a sentence is tagged as "(TOP*" and the last word is tagged as "*)" and all intermediate words are tagged with a "*".
    - predicate_lemma = The predicate lemma is mentioned for the rows for which we have semantic role information or word sense information. All other rows are marked with a "-".
    - predicate_framenet_id = This is the PropBank frameset ID of the predicate in predicate_lemma.
    - word_sense = This is the word sense of the word in Column tokens.
    - speaker = This is the speaker or author name where available.
    - ner_tags = These columns identifies the spans representing various named entities. For documents which do not have named entity annotation, each line is represented with an "*".
    - verbal_predicates = There is one column each of predicate argument structure information for the predicate mentioned in predicate_lemma. If there are no predicates tagged in a sentence this is a single column with all rows marked with an "*".

### Data Splits

Dev and Test Split available

## Dataset Creation

### Curation Rationale

The WinoBias dataset was introduced in 2018 (see paper), with its original task being *coreference resolution*, which is a task that aims to identify mentions that refer to the same entity or person.

### Source Data

#### Initial Data Collection and Normalization



#### Who are the source language producers?

 The dataset was created by researchers familiar with the WinoBias project, based on two prototypical templates provided by the authors, in which entities interact in plausible ways.

### Annotations

#### Annotation process



#### Who are the annotators?

"Researchers familiar with the [WinoBias] project"

### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases

Recent work has shown that this dataset contains grammatical issues, incorrect or ambiguous labels, and stereotype conflation, among other limitations. 

### Other Known Limitations



## Additional Information

### Dataset Curators

Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez and Kai-Wei Chan

### Licensing Information

MIT Licence



@article{DBLP:journals/corr/abs-1804-06876,
  author    = {Jieyu Zhao and
               Tianlu Wang and
               Mark Yatskar and
               Vicente Ordonez and
               Kai{-}Wei Chang},
  title     = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods},
  journal   = {CoRR},
  volume    = {abs/1804.06876},
  year      = {2018},
  url       = {URL
  archivePrefix = {arXiv},
  eprint    = {1804.06876},
  timestamp = {Mon, 13 Aug 2018 16:47:01 +0200},
  biburl    = {URL
  bibsource = {dblp computer science bibliography, URL}
}

### Contributions

Thanks to @akshayb7 for adding this dataset. Updated by @JieyuZhao.
# Dataset Card for Wino_Bias dataset ## 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: WinoBias
- Repository:
- Paper: Arxiv
- Leaderboard:
- Point of Contact: ### Dataset Summary

WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.
The corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). ### Supported Tasks and Leaderboards

The underlying task is coreference resolution. ### Languages

English ## Dataset Structure ### Data Instances

The dataset has 4 subsets: 'type1_pro', 'type1_anti', 'type2_pro' and 'type2_anti'.

The '*_pro' subsets contain sentences that reinforce gender stereotypes (e.g. mechanics are male, nurses are female), whereas the '*_anti' datasets contain "anti-stereotypical" sentences  (e.g. mechanics are female, nurses are male).

The 'type1' (*WB-Knowledge*) subsets contain sentences for which world knowledge is necessary to resolve the co-references, and 'type2' (*WB-Syntax*) subsets require only the syntactic information present in the sentence to resolve them. ### Data Fields

    - document_id = This is a variation on the document filename
    - part_number = Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
    - word_num = This is the word index of the word in that sentence.
    - tokens = This is the token as segmented/tokenized in the Treebank.
    - pos_tags = This is the Penn Treebank style part of speech. When parse information is missing, all part of speeches except the one for which there is some sense or proposition annotation   are marked with a XX tag. The verb is marked with just a VERB tag.
    - parse_bit = This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column. When the parse information is missing, the first word of a sentence is tagged as "(TOP*" and the last word is tagged as "*)" and all intermediate words are tagged with a "*".
    - predicate_lemma = The predicate lemma is mentioned for the rows for which we have semantic role information or word sense information. All other rows are marked with a "-".
    - predicate_framenet_id = This is the PropBank frameset ID of the predicate in predicate_lemma.
    - word_sense = This is the word sense of the word in Column tokens.
    - speaker = This is the speaker or author name where available.
    - ner_tags = These columns identifies the spans representing various named entities. For documents which do not have named entity annotation, each line is represented with an "*".
    - verbal_predicates = There is one column each of predicate argument structure information for the predicate mentioned in predicate_lemma. If there are no predicates tagged in a sentence this is a single column with all rows marked with an "*". ### Data Splits

Dev and Test Split available ## Dataset Creation ### Curation Rationale

The WinoBias dataset was introduced in 2018 (see paper), with its original task being *coreference resolution*, which is a task that aims to identify mentions that refer to the same entity or person. ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers?

 The dataset was created by researchers familiar with the WinoBias project, based on two prototypical templates provided by the authors, in which entities interact in plausible ways. ### Annotations #### Annotation process #### Who are the annotators?

"Researchers familiar with the [WinoBias] project" ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases

Recent work has shown that this dataset contains grammatical issues, incorrect or ambiguous labels, and stereotype conflation, among other limitations. ### Other Known Limitations ## Additional Information ### Dataset Curators

Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez and Kai-Wei Chan ### Licensing Information

MIT Licence



@article{DBLP:journals/corr/abs-1804-06876,
  author    = {Jieyu Zhao and
               Tianlu Wang and
               Mark Yatskar and
               Vicente Ordonez and
               Kai{-}Wei Chang},
  title     = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods},
  journal   = {CoRR},
  volume    = {abs/1804.06876},
  year      = {2018},
  url       = {URL
  archivePrefix = {arXiv},
  eprint    = {1804.06876},
  timestamp = {Mon, 13 Aug 2018 16:47:01 +0200},
  biburl    = {URL
  bibsource = {dblp computer science bibliography, URL}
} ### Contributions

Thanks to @akshayb7 for adding this dataset. Updated by @JieyuZhao.
TAGS
#task_categories-token-classification #task_ids-coreference-resolution #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-mit #arxiv-1804.06876 #region-us 
 # Dataset Card for Wino_Bias dataset ## 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: WinoBias
- Repository:
- Paper: Arxiv
- Leaderboard:
- Point of Contact: ### Dataset Summary

WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.
The corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). ### Supported Tasks and Leaderboards

The underlying task is coreference resolution. ### Languages

English ## Dataset Structure ### Data Instances

The dataset has 4 subsets: 'type1_pro', 'type1_anti', 'type2_pro' and 'type2_anti'.

The '*_pro' subsets contain sentences that reinforce gender stereotypes (e.g. mechanics are male, nurses are female), whereas the '*_anti' datasets contain "anti-stereotypical" sentences  (e.g. mechanics are female, nurses are male).

The 'type1' (*WB-Knowledge*) subsets contain sentences for which world knowledge is necessary to resolve the co-references, and 'type2' (*WB-Syntax*) subsets require only the syntactic information present in the sentence to resolve them. ### Data Fields

    - document_id = This is a variation on the document filename
    - part_number = Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
    - word_num = This is the word index of the word in that sentence.
    - tokens = This is the token as segmented/tokenized in the Treebank.
    - pos_tags = This is the Penn Treebank style part of speech. When parse information is missing, all part of speeches except the one for which there is some sense or proposition annotation   are marked with a XX tag. The verb is marked with just a VERB tag.
    - parse_bit = This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column. When the parse information is missing, the first word of a sentence is tagged as "(TOP*" and the last word is tagged as "*)" and all intermediate words are tagged with a "*".
    - predicate_lemma = The predicate lemma is mentioned for the rows for which we have semantic role information or word sense information. All other rows are marked with a "-".
    - predicate_framenet_id = This is the PropBank frameset ID of the predicate in predicate_lemma.
    - word_sense = This is the word sense of the word in Column tokens.
    - speaker = This is the speaker or author name where available.
    - ner_tags = These columns identifies the spans representing various named entities. For documents which do not have named entity annotation, each line is represented with an "*".
    - verbal_predicates = There is one column each of predicate argument structure information for the predicate mentioned in predicate_lemma. If there are no predicates tagged in a sentence this is a single column with all rows marked with an "*". ### Data Splits

Dev and Test Split available ## Dataset Creation ### Curation Rationale

The WinoBias dataset was introduced in 2018 (see paper), with its original task being *coreference resolution*, which is a task that aims to identify mentions that refer to the same entity or person. ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers?

 The dataset was created by researchers familiar with the WinoBias project, based on two prototypical templates provided by the authors, in which entities interact in plausible ways. ### Annotations #### Annotation process #### Who are the annotators?

"Researchers familiar with the [WinoBias] project" ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases

Recent work has shown that this dataset contains grammatical issues, incorrect or ambiguous labels, and stereotype conflation, among other limitations. ### Other Known Limitations ## Additional Information ### Dataset Curators

Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez and Kai-Wei Chan ### Licensing Information

MIT Licence



@article{DBLP:journals/corr/abs-1804-06876,
  author    = {Jieyu Zhao and
               Tianlu Wang and
               Mark Yatskar and
               Vicente Ordonez and
               Kai{-}Wei Chang},
  title     = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods},
  journal   = {CoRR},
  volume    = {abs/1804.06876},
  year      = {2018},
  url       = {URL
  archivePrefix = {arXiv},
  eprint    = {1804.06876},
  timestamp = {Mon, 13 Aug 2018 16:47:01 +0200},
  biburl    = {URL
  bibsource = {dblp computer science bibliography, URL}
} ### Contributions

Thanks to @akshayb7 for adding this dataset. Updated by @JieyuZhao.
passage: TAGS
#task_categories-token-classification #task_ids-coreference-resolution #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-mit #arxiv-1804.06876 #region-us 
# Dataset Card for Wino_Bias dataset## 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: WinoBias
- Repository:
- Paper: Arxiv
- Leaderboard:
- Point of Contact:### Dataset Summary

WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.
The corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter).### Supported Tasks and Leaderboards

The underlying task is coreference resolution.### Languages

English## Dataset Structure passage: ### Data Instances

The dataset has 4 subsets: 'type1_pro', 'type1_anti', 'type2_pro' and 'type2_anti'.

The '*_pro' subsets contain sentences that reinforce gender stereotypes (e.g. mechanics are male, nurses are female), whereas the '*_anti' datasets contain "anti-stereotypical" sentences  (e.g. mechanics are female, nurses are male).

The 'type1' (*WB-Knowledge*) subsets contain sentences for which world knowledge is necessary to resolve the co-references, and 'type2' (*WB-Syntax*) subsets require only the syntactic information present in the sentence to resolve them.### Data Fields

    - document_id = This is a variation on the document filename
    - part_number = Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
    - word_num = This is the word index of the word in that sentence.
    - tokens = This is the token as segmented/tokenized in the Treebank.
    - pos_tags = This is the Penn Treebank style part of speech. When parse information is missing, all part of speeches except the one for which there is some sense or proposition annotation   are marked with a XX tag. The verb is marked with just a VERB tag.
    - parse_bit = This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column. When the parse information is missing, the first word of a sentence is tagged as "(TOP*" and the last word is tagged as "*)" and all intermediate words are tagged with a "*".
    - predicate_lemma = The predicate lemma is mentioned for the rows for which we have semantic role information or word sense information. All other rows are marked with a "-".
    - predicate_framenet_id = This is the PropBank frameset ID of the predicate in predicate_lemma.
    - word_sense = This is the word sense of the word in Column tokens.
    - speaker = This is the speaker or author name where available.
    - ner_tags = These columns identifies the spans representing various named entities. For documents which do not have named entity annotation, each line is represented with an "*".
    - verbal_predicates = There is one column each of predicate argument structure information for the predicate mentioned in predicate_lemma. If there are no predicates tagged in a sentence this is a single column with all rows marked with an "*".