Dataset Card for Multi-Dimensional Gender Bias Classification

Dataset Summary

Machine learning models are trained to find patterns in data. NLP models can inadvertently learn socially undesirable patterns when training on gender biased text. In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions: bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information. In addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites. Distinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers. We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models, detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.

Supported Tasks and Leaderboards

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Languages

The data is in English (en)

Dataset Structure

Data Instances

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Data Fields

The data has the following features.

For the new_data config:

  • text: the text to be classified
  • original: the text before reformulation
  • labels: a list of classification labels, with possible values including ABOUT:female, ABOUT:male, PARTNER:female, PARTNER:male, SELF:female.
  • class_type: a classification label, with possible values including about, partner, self.
  • turker_gender: a classification label, with possible values including man, woman, nonbinary, prefer not to say, no answer.

For the other annotated datasets:

  • text: the text to be classified.
  • gender: a classification label, with possible values including gender-neutral, female, male.

For the _inferred configurations:

  • text: the text to be classified.
  • binary_label: a classification label, with possible values including ABOUT:female, ABOUT:male.
  • binary_score: a score between 0 and 1.
  • ternary_label: a classification label, with possible values including ABOUT:female, ABOUT:male, ABOUT:gender-neutral.
  • ternary_score: a score between 0 and 1.

Data Splits

The different parts of the data can be accessed through the different configurations:

  • gendered_words: A list of common nouns with a masculine and feminine variant.
  • new_data: Sentences reformulated and annotated along all three axes.
  • funpedia, wizard: Sentences from Funpedia and Wizards of Wikipedia annotated with ABOUT gender with entity gender information.
  • image_chat: sentences about images annotated with ABOUT gender based on gender information from the entities in the image
  • convai2_inferred, light_inferred, opensubtitles_inferred, yelp_inferred: Data from several source datasets with ABOUT annotations inferred by a trined classifier.

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

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Contributions

Thanks to @yjernite for adding this dataset.

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