Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
ArXiv:
Tags:
gender-bias
License:
# coding=utf-8 | |
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
"""Multi-Dimensional Gender Bias classification""" | |
from __future__ import absolute_import, division, print_function | |
import json | |
import os | |
import datasets | |
# TODO: Add BibTeX citation | |
# Find for instance the citation on arxiv or on the dataset repo/website | |
_CITATION = """\ | |
@inproceedings{md_gender_bias, | |
author = {Emily Dinan and | |
Angela Fan and | |
Ledell Wu and | |
Jason Weston and | |
Douwe Kiela and | |
Adina Williams}, | |
editor = {Bonnie Webber and | |
Trevor Cohn and | |
Yulan He and | |
Yang Liu}, | |
title = {Multi-Dimensional Gender Bias Classification}, | |
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural | |
Language Processing, {EMNLP} 2020, Online, November 16-20, 2020}, | |
pages = {314--331}, | |
publisher = {Association for Computational Linguistics}, | |
year = {2020}, | |
url = {https://www.aclweb.org/anthology/2020.emnlp-main.23/} | |
} | |
""" | |
# TODO: Add description of the dataset here | |
# You can copy an official description | |
_DESCRIPTION = """\ | |
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. | |
""" | |
_HOMEPAGE = "https://parl.ai/projects/md_gender/" | |
_LICENSE = "MIT License" | |
_URL = "http://parl.ai/downloads/md_gender/gend_multiclass_10072020.tgz" | |
_CONF_FILES = { | |
"funpedia": { | |
"train": "funpedia/train.jsonl", | |
"validation": "funpedia/valid.jsonl", | |
"test": "funpedia/test.jsonl", | |
}, | |
"image_chat": { | |
"train": "image_chat/engaging_imagechat_gender_captions_hashed.test.jsonl", | |
"validation": "image_chat/engaging_imagechat_gender_captions_hashed.train.jsonl", | |
"test": "image_chat/engaging_imagechat_gender_captions_hashed.valid.jsonl", | |
}, | |
"wizard": { | |
"train": "wizard/train.jsonl", | |
"validation": "wizard/valid.jsonl", | |
"test": "wizard/test.jsonl", | |
}, | |
"convai2_inferred": { | |
"train": ( | |
"inferred_about/convai2_train_binary.txt", | |
"inferred_about/convai2_train.txt", | |
), | |
"validation": ( | |
"inferred_about/convai2_valid_binary.txt", | |
"inferred_about/convai2_valid.txt", | |
), | |
"test": ( | |
"inferred_about/convai2_test_binary.txt", | |
"inferred_about/convai2_test.txt", | |
), | |
}, | |
"light_inferred": { | |
"train": ( | |
"inferred_about/light_train_binary.txt", | |
"inferred_about/light_train.txt", | |
), | |
"validation": ( | |
"inferred_about/light_valid_binary.txt", | |
"inferred_about/light_valid.txt", | |
), | |
"test": ( | |
"inferred_about/light_test_binary.txt", | |
"inferred_about/light_test.txt", | |
), | |
}, | |
"opensubtitles_inferred": { | |
"train": ( | |
"inferred_about/opensubtitles_train_binary.txt", | |
"inferred_about/opensubtitles_train.txt", | |
), | |
"validation": ( | |
"inferred_about/opensubtitles_valid_binary.txt", | |
"inferred_about/opensubtitles_valid.txt", | |
), | |
"test": ( | |
"inferred_about/opensubtitles_test_binary.txt", | |
"inferred_about/opensubtitles_test.txt", | |
), | |
}, | |
"yelp_inferred": { | |
"train": ( | |
"inferred_about/yelp_train_binary.txt", | |
"", | |
), | |
"validation": ( | |
"inferred_about/yelp_valid_binary.txt", | |
"", | |
), | |
"test": ( | |
"inferred_about/yelp_test_binary.txt", | |
"", | |
), | |
}, | |
} | |
class MdGenderBias(datasets.GeneratorBasedBuilder): | |
"""Multi-Dimensional Gender Bias classification""" | |
VERSION = datasets.Version("1.0.0") | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig( | |
name="gendered_words", | |
version=VERSION, | |
description="A list of common nouns with a masculine and feminine variant.", | |
), | |
datasets.BuilderConfig( | |
name="name_genders", | |
version=VERSION, | |
description="A list of first names with their gender attribution by year in the US.", | |
), | |
datasets.BuilderConfig( | |
name="new_data", version=VERSION, description="Some data reformulated and annotated along all three axes." | |
), | |
datasets.BuilderConfig( | |
name="funpedia", | |
version=VERSION, | |
description="Data from Funpedia with ABOUT annotations based on Funpedia information on an entity's gender.", | |
), | |
datasets.BuilderConfig( | |
name="image_chat", | |
version=VERSION, | |
description="Data from ImageChat with ABOUT annotations based on image recognition.", | |
), | |
datasets.BuilderConfig( | |
name="wizard", | |
version=VERSION, | |
description="Data from WizardsOfWikipedia with ABOUT annotations based on Wikipedia information on an entity's gender.", | |
), | |
datasets.BuilderConfig( | |
name="convai2_inferred", | |
version=VERSION, | |
description="Data from the ConvAI2 challenge with ABOUT annotations inferred by a trined classifier.", | |
), | |
datasets.BuilderConfig( | |
name="light_inferred", | |
version=VERSION, | |
description="Data from LIGHT with ABOUT annotations inferred by a trined classifier.", | |
), | |
datasets.BuilderConfig( | |
name="opensubtitles_inferred", | |
version=VERSION, | |
description="Data from OpenSubtitles with ABOUT annotations inferred by a trined classifier.", | |
), | |
datasets.BuilderConfig( | |
name="yelp_inferred", | |
version=VERSION, | |
description="Data from Yelp reviews with ABOUT annotations inferred by a trined classifier.", | |
), | |
] | |
DEFAULT_CONFIG_NAME = ( | |
"new_data" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
) | |
def _info(self): | |
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset | |
if ( | |
self.config.name == "gendered_words" | |
): # This is the name of the configuration selected in BUILDER_CONFIGS above | |
features = datasets.Features( | |
{ | |
"word_masculine": datasets.Value("string"), | |
"word_feminine": datasets.Value("string"), | |
} | |
) | |
elif self.config.name == "name_genders": | |
features = datasets.Features( | |
{ | |
"name": datasets.Value("string"), | |
"assigned_gender": datasets.ClassLabel(names=["M", "F"]), | |
"count": datasets.Value("int32"), | |
} | |
) | |
elif self.config.name == "new_data": | |
features = datasets.Features( | |
{ | |
"text": datasets.Value("string"), | |
"original": datasets.Value("string"), | |
"labels": [ | |
datasets.ClassLabel( | |
names=[ | |
"ABOUT:female", | |
"ABOUT:male", | |
"PARTNER:female", | |
"PARTNER:male", | |
"SELF:female", | |
"SELF:male", | |
] | |
) | |
], | |
"class_type": datasets.ClassLabel(names=["about", "partner", "self"]), | |
"turker_gender": datasets.ClassLabel( | |
names=["man", "woman", "nonbinary", "prefer not to say", "no answer"] | |
), | |
"episode_done": datasets.Value("bool_"), | |
"confidence": datasets.Value("string"), | |
} | |
) | |
elif self.config.name == "funpedia": | |
features = datasets.Features( | |
{ | |
"text": datasets.Value("string"), | |
"title": datasets.Value("string"), | |
"persona": datasets.Value("string"), | |
"gender": datasets.ClassLabel(names=["gender-neutral", "female", "male"]), | |
} | |
) | |
elif self.config.name == "image_chat": | |
features = datasets.Features( | |
{ | |
"caption": datasets.Value("string"), | |
"id": datasets.Value("string"), | |
"male": datasets.Value("bool_"), | |
"female": datasets.Value("bool_"), | |
} | |
) | |
elif self.config.name == "wizard": | |
features = datasets.Features( | |
{ | |
"text": datasets.Value("string"), | |
"chosen_topic": datasets.Value("string"), | |
"gender": datasets.ClassLabel(names=["gender-neutral", "female", "male"]), | |
} | |
) | |
elif self.config.name == "yelp_inferred": | |
features = datasets.Features( | |
{ | |
"text": datasets.Value("string"), | |
"binary_label": datasets.ClassLabel(names=["ABOUT:female", "ABOUT:male"]), | |
"binary_score": datasets.Value("float"), | |
} | |
) | |
else: # data with inferred labels | |
features = datasets.Features( | |
{ | |
"text": datasets.Value("string"), | |
"binary_label": datasets.ClassLabel(names=["ABOUT:female", "ABOUT:male"]), | |
"binary_score": datasets.Value("float"), | |
"ternary_label": datasets.ClassLabel(names=["ABOUT:female", "ABOUT:male", "ABOUT:gender-neutral"]), | |
"ternary_score": datasets.Value("float"), | |
} | |
) | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=features, # Here we define them above because they are different between the two configurations | |
supervised_keys=None, | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
data_dir = os.path.join(dl_manager.download_and_extract(_URL), "data_to_release") | |
if self.config.name == "gendered_words": | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"filepath": None, | |
"filepath_pair": ( | |
os.path.join(data_dir, "word_list/male_word_file.txt"), | |
os.path.join(data_dir, "word_list/female_word_file.txt"), | |
), | |
}, | |
) | |
] | |
elif self.config.name == "name_genders": | |
return [ | |
datasets.SplitGenerator( | |
name=f"yob{yob}", | |
gen_kwargs={ | |
"filepath": os.path.join(data_dir, f"names/yob{yob}.txt"), | |
"filepath_pair": None, | |
}, | |
) | |
for yob in range(1880, 2019) | |
] | |
elif self.config.name == "new_data": | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"filepath": os.path.join(data_dir, "new_data/data.jsonl"), | |
"filepath_pair": None, | |
}, | |
) | |
] | |
elif self.config.name in ["funpedia", "image_chat", "wizard"]: | |
return [ | |
datasets.SplitGenerator( | |
name=spl, | |
gen_kwargs={ | |
"filepath": os.path.join(data_dir, fname), | |
"filepath_pair": None, | |
}, | |
) | |
for spl, fname in _CONF_FILES[self.config.name].items() | |
] | |
else: | |
return [ | |
datasets.SplitGenerator( | |
name=spl, | |
gen_kwargs={ | |
"filepath": None, | |
"filepath_pair": ( | |
os.path.join(data_dir, fname_1), | |
os.path.join(data_dir, fname_2), | |
), | |
}, | |
) | |
for spl, (fname_1, fname_2) in _CONF_FILES[self.config.name].items() | |
] | |
def _generate_examples(self, filepath, filepath_pair): | |
if self.config.name == "gendered_words": | |
with open(filepath_pair[0], encoding="utf-8") as f_m: | |
with open(filepath_pair[1], encoding="utf-8") as f_f: | |
for id_, (l_m, l_f) in enumerate(zip(f_m, f_f)): | |
yield id_, { | |
"word_masculine": l_m.strip(), | |
"word_feminine": l_f.strip(), | |
} | |
elif self.config.name == "name_genders": | |
with open(filepath, encoding="utf-8") as f: | |
for id_, line in enumerate(f): | |
name, g, ct = line.strip().split(",") | |
yield id_, { | |
"name": name, | |
"assigned_gender": g, | |
"count": int(ct), | |
} | |
elif "_inferred" in self.config.name: | |
with open(filepath_pair[0], encoding="utf-8") as f_b: | |
if "yelp" in self.config.name: | |
for id_, line_b in enumerate(f_b): | |
text_b, label_b, score_b = line_b.split("\t") | |
yield id_, { | |
"text": text_b, | |
"binary_label": label_b, | |
"binary_score": float(score_b.strip()), | |
} | |
else: | |
with open(filepath_pair[1], encoding="utf-8") as f_t: | |
for id_, (line_b, line_t) in enumerate(zip(f_b, f_t)): | |
text_b, label_b, score_b = line_b.split("\t") | |
text_t, label_t, score_t = line_t.split("\t") | |
yield id_, { | |
"text": text_b, | |
"binary_label": label_b, | |
"binary_score": float(score_b.strip()), | |
"ternary_label": label_t, | |
"ternary_score": float(score_t.strip()), | |
} | |
else: | |
with open(filepath, encoding="utf-8") as f: | |
for id_, line in enumerate(f): | |
example = json.loads(line.strip()) | |
if "turker_gender" in example and example["turker_gender"] is None: | |
example["turker_gender"] = "no answer" | |
yield id_, example | |