Datasets:
md_gender_bias

- {"gendered_words": {"description": "Machine learning models are trained to find patterns in data.\nNLP models can inadvertently learn socially undesirable patterns when training on gender biased text.\nIn this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions:\nbias 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.\nUsing this fine-grained framework, we automatically annotate eight large scale datasets with gender information.\nIn addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites.\nDistinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers.\nWe show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models,\ndetecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.\n", "citation": "@inproceedings{md_gender_bias,\n author = {Emily Dinan and\n Angela Fan and\n Ledell Wu and\n Jason Weston and\n Douwe Kiela and\n Adina Williams},\n editor = {Bonnie Webber and\n Trevor Cohn and\n Yulan He and\n Yang Liu},\n title = {Multi-Dimensional Gender Bias Classification},\n booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural\n Language Processing, {EMNLP} 2020, Online, November 16-20, 2020},\n pages = {314--331},\n publisher = {Association for Computational Linguistics},\n year = {2020},\n url = {https://www.aclweb.org/anthology/2020.emnlp-main.23/}\n}\n", "homepage": "https://parl.ai/projects/md_gender/", "license": "MIT License", "features": {"word_masculine": {"dtype": "string", "id": null, "_type": "Value"}, "word_feminine": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "md_gender_bias", "config_name": "gendered_words", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4988, "num_examples": 222, "dataset_name": "md_gender_bias"}}, "download_checksums": {"http://parl.ai/downloads/md_gender/gend_multiclass_10072020.tgz": {"num_bytes": 232629010, "checksum": "c2c03257c53497b9e453600201fc7245b55dec1d98965093b4657fdb54822e9d"}}, "download_size": 232629010, "post_processing_size": null, "dataset_size": 4988, "size_in_bytes": 232633998}, "name_genders": {"description": "Machine learning models are trained to find patterns in data.\nNLP models can inadvertently learn socially undesirable patterns when training on gender biased text.\nIn this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions:\nbias 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.\nUsing this fine-grained framework, we automatically annotate eight large scale datasets with gender information.\nIn addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites.\nDistinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers.\nWe show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models,\ndetecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.\n", "citation": "@inproceedings{md_gender_bias,\n author = {Emily Dinan and\n Angela Fan and\n Ledell Wu and\n Jason Weston and\n Douwe Kiela and\n Adina Williams},\n editor = {Bonnie Webber and\n Trevor Cohn and\n Yulan He and\n Yang Liu},\n title = {Multi-Dimensional Gender Bias Classification},\n booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural\n Language Processing, {EMNLP} 2020, Online, November 16-20, 2020},\n pages = {314--331},\n publisher = {Association for Computational Linguistics},\n year = {2020},\n url = {https://www.aclweb.org/anthology/2020.emnlp-main.23/}\n}\n", "homepage": "https://parl.ai/projects/md_gender/", "license": "MIT License", "features": {"name": {"dtype": "string", "id": null, "_type": "Value"}, "assigned_gender": {"num_classes": 2, "names": ["M", "F"], "names_file": null, "id": null, "_type": "ClassLabel"}, "count": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "md_gender_bias", "config_name": "name_genders", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"yob1880": {"name": "yob1880", "num_bytes": 43404, "num_examples": 2000, "dataset_name": "md_gender_bias"}, "yob1881": {"name": "yob1881", "num_bytes": 41944, "num_examples": 1935, 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