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Gender Profession Data

Data

This repository contains the data used in the paper Faithful Target Attribute Prediction in Neural Machine Translation by Xing Niu, Georgiana Dinu, Prashant Mathur and Anna Currey.

We combine three profession lists collected by Rudinger et. al. (2019), Zhao et. al. (2018), and Prates et. al. (2022) which yields 1054 distinct English gender-neutral professions. These professions can be either a single word (e.g., singer) or a phrase (e.g., flight attendant).

English professions list are compiled from the following links:

Translations

We requested both masculine and feminine translations in French from paid professional translators. The instruction to translators contains examples:

  • If the profession is driver we would like both conducteur and conductrice. Ideally we want equivalent masculine and feminine words if such equivalents exist (so not chauffeur and conductrice).

All translations were done by a single translator and QA'd by a second translator. The translators returns 861 gender-distinct translation pairs, meaning masculine and feminine translations were not the same. In this repo, we release both original and gender-distinct translation pairs.

Data Statistics

# Segments # SrcWords
original 1054 2548
processed 861 2163

Repo Structure

ROOT
|
|____original  # contains full original data
|    |
|    |____source.en   # list of professions in English
|    |____fem_ref.fr  # translations of English phrases in Feminine form
|    |____masc_ref.fr # translations of English phrases in Masculine form
|
|____processed  # contains only data with different feminine and masculine references
|    |
|    |____source.en   # list of professions in English
|    |____fem_ref.fr  # translations of English phrases in Feminine form
|    |____masc_ref.fr # translations of English phrases in Masculine form
|
|____README.md

Citing

@misc{niu2021faithful,
      title={Faithful Target Attribute Prediction in Neural Machine Translation}, 
      author={Xing Niu and Georgiana Dinu and Prashant Mathur and Anna Currey},
      year={2021},
      eprint={2109.12105},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Security

See CONTRIBUTING for more information.

License Summary

This project is licensed under the CC-BY-NC-4.0 License. The documentation is made available under the Creative Commons Attribution-ShareAlike 4.0 International License. See the LICENSE file.

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