Datasets:
annotations_creators:
- crowdsourced
- found
- machine-generated
language_creators:
- crowdsourced
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
- 1M<n<10M
- n<1K
source_datasets:
- extended|other-convai2
- extended|other-light
- extended|other-opensubtitles
- extended|other-yelp
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: md-gender
pretty_name: Multi-Dimensional Gender Bias Classification
tags:
- gender-bias
dataset_info:
- config_name: gendered_words
features:
- name: word_masculine
dtype: string
- name: word_feminine
dtype: string
splits:
- name: train
num_bytes: 4988
num_examples: 222
download_size: 232629010
dataset_size: 4988
- config_name: name_genders
features:
- name: name
dtype: string
- name: assigned_gender
dtype:
class_label:
names:
'0': M
'1': F
- name: count
dtype: int32
splits:
- name: yob1880
num_bytes: 43404
num_examples: 2000
- name: yob1881
num_bytes: 41944
num_examples: 1935
- name: yob1882
num_bytes: 46211
num_examples: 2127
- name: yob1883
num_bytes: 45221
num_examples: 2084
- name: yob1884
num_bytes: 49886
num_examples: 2297
- name: yob1885
num_bytes: 49810
num_examples: 2294
- name: yob1886
num_bytes: 51935
num_examples: 2392
- name: yob1887
num_bytes: 51458
num_examples: 2373
- name: yob1888
num_bytes: 57531
num_examples: 2651
- name: yob1889
num_bytes: 56177
num_examples: 2590
- name: yob1890
num_bytes: 58509
num_examples: 2695
- name: yob1891
num_bytes: 57767
num_examples: 2660
- name: yob1892
num_bytes: 63493
num_examples: 2921
- name: yob1893
num_bytes: 61525
num_examples: 2831
- name: yob1894
num_bytes: 63927
num_examples: 2941
- name: yob1895
num_bytes: 66346
num_examples: 3049
- name: yob1896
num_bytes: 67224
num_examples: 3091
- name: yob1897
num_bytes: 65886
num_examples: 3028
- name: yob1898
num_bytes: 71088
num_examples: 3264
- name: yob1899
num_bytes: 66225
num_examples: 3042
- name: yob1900
num_bytes: 81305
num_examples: 3730
- name: yob1901
num_bytes: 68723
num_examples: 3153
- name: yob1902
num_bytes: 73321
num_examples: 3362
- name: yob1903
num_bytes: 74019
num_examples: 3389
- name: yob1904
num_bytes: 77751
num_examples: 3560
- name: yob1905
num_bytes: 79802
num_examples: 3655
- name: yob1906
num_bytes: 79392
num_examples: 3633
- name: yob1907
num_bytes: 86342
num_examples: 3948
- name: yob1908
num_bytes: 87965
num_examples: 4018
- name: yob1909
num_bytes: 92591
num_examples: 4227
- name: yob1910
num_bytes: 101491
num_examples: 4629
- name: yob1911
num_bytes: 106787
num_examples: 4867
- name: yob1912
num_bytes: 139448
num_examples: 6351
- name: yob1913
num_bytes: 153110
num_examples: 6968
- name: yob1914
num_bytes: 175167
num_examples: 7965
- name: yob1915
num_bytes: 205921
num_examples: 9357
- name: yob1916
num_bytes: 213468
num_examples: 9696
- name: yob1917
num_bytes: 218446
num_examples: 9913
- name: yob1918
num_bytes: 229209
num_examples: 10398
- name: yob1919
num_bytes: 228656
num_examples: 10369
- name: yob1920
num_bytes: 237286
num_examples: 10756
- name: yob1921
num_bytes: 239616
num_examples: 10857
- name: yob1922
num_bytes: 237569
num_examples: 10756
- name: yob1923
num_bytes: 235046
num_examples: 10643
- name: yob1924
num_bytes: 240113
num_examples: 10869
- name: yob1925
num_bytes: 235098
num_examples: 10638
- name: yob1926
num_bytes: 230970
num_examples: 10458
- name: yob1927
num_bytes: 230004
num_examples: 10406
- name: yob1928
num_bytes: 224583
num_examples: 10159
- name: yob1929
num_bytes: 217057
num_examples: 9820
- name: yob1930
num_bytes: 216352
num_examples: 9791
- name: yob1931
num_bytes: 205361
num_examples: 9298
- name: yob1932
num_bytes: 207268
num_examples: 9381
- name: yob1933
num_bytes: 199031
num_examples: 9013
- name: yob1934
num_bytes: 202758
num_examples: 9180
- name: yob1935
num_bytes: 199614
num_examples: 9037
- name: yob1936
num_bytes: 196379
num_examples: 8894
- name: yob1937
num_bytes: 197757
num_examples: 8946
- name: yob1938
num_bytes: 199603
num_examples: 9032
- name: yob1939
num_bytes: 196979
num_examples: 8918
- name: yob1940
num_bytes: 198141
num_examples: 8961
- name: yob1941
num_bytes: 200858
num_examples: 9085
- name: yob1942
num_bytes: 208363
num_examples: 9425
- name: yob1943
num_bytes: 207940
num_examples: 9408
- name: yob1944
num_bytes: 202227
num_examples: 9152
- name: yob1945
num_bytes: 199478
num_examples: 9025
- name: yob1946
num_bytes: 214614
num_examples: 9705
- name: yob1947
num_bytes: 229327
num_examples: 10371
- name: yob1948
num_bytes: 226615
num_examples: 10241
- name: yob1949
num_bytes: 227278
num_examples: 10269
- name: yob1950
num_bytes: 227946
num_examples: 10303
- name: yob1951
num_bytes: 231613
num_examples: 10462
- name: yob1952
num_bytes: 235483
num_examples: 10646
- name: yob1953
num_bytes: 239654
num_examples: 10837
- name: yob1954
num_bytes: 242389
num_examples: 10968
- name: yob1955
num_bytes: 245652
num_examples: 11115
- name: yob1956
num_bytes: 250674
num_examples: 11340
- name: yob1957
num_bytes: 255370
num_examples: 11564
- name: yob1958
num_bytes: 254520
num_examples: 11522
- name: yob1959
num_bytes: 260051
num_examples: 11767
- name: yob1960
num_bytes: 263474
num_examples: 11921
- name: yob1961
num_bytes: 269493
num_examples: 12182
- name: yob1962
num_bytes: 270244
num_examples: 12209
- name: yob1963
num_bytes: 271872
num_examples: 12282
- name: yob1964
num_bytes: 274590
num_examples: 12397
- name: yob1965
num_bytes: 264889
num_examples: 11952
- name: yob1966
num_bytes: 269321
num_examples: 12151
- name: yob1967
num_bytes: 274867
num_examples: 12397
- name: yob1968
num_bytes: 286774
num_examples: 12936
- name: yob1969
num_bytes: 304909
num_examples: 13749
- name: yob1970
num_bytes: 328047
num_examples: 14779
- name: yob1971
num_bytes: 339657
num_examples: 15295
- name: yob1972
num_bytes: 342321
num_examples: 15412
- name: yob1973
num_bytes: 348414
num_examples: 15682
- name: yob1974
num_bytes: 361188
num_examples: 16249
- name: yob1975
num_bytes: 376491
num_examples: 16944
- name: yob1976
num_bytes: 386565
num_examples: 17391
- name: yob1977
num_bytes: 403994
num_examples: 18175
- name: yob1978
num_bytes: 405430
num_examples: 18231
- name: yob1979
num_bytes: 423423
num_examples: 19039
- name: yob1980
num_bytes: 432317
num_examples: 19452
- name: yob1981
num_bytes: 432980
num_examples: 19475
- name: yob1982
num_bytes: 437986
num_examples: 19694
- name: yob1983
num_bytes: 431531
num_examples: 19407
- name: yob1984
num_bytes: 434085
num_examples: 19506
- name: yob1985
num_bytes: 447113
num_examples: 20085
- name: yob1986
num_bytes: 460315
num_examples: 20657
- name: yob1987
num_bytes: 477677
num_examples: 21406
- name: yob1988
num_bytes: 499347
num_examples: 22367
- name: yob1989
num_bytes: 531020
num_examples: 23775
- name: yob1990
num_bytes: 552114
num_examples: 24716
- name: yob1991
num_bytes: 560932
num_examples: 25109
- name: yob1992
num_bytes: 568151
num_examples: 25427
- name: yob1993
num_bytes: 579778
num_examples: 25966
- name: yob1994
num_bytes: 580223
num_examples: 25997
- name: yob1995
num_bytes: 581949
num_examples: 26080
- name: yob1996
num_bytes: 589131
num_examples: 26423
- name: yob1997
num_bytes: 601284
num_examples: 26970
- name: yob1998
num_bytes: 621587
num_examples: 27902
- name: yob1999
num_bytes: 635355
num_examples: 28552
- name: yob2000
num_bytes: 662398
num_examples: 29772
- name: yob2001
num_bytes: 673111
num_examples: 30274
- name: yob2002
num_bytes: 679392
num_examples: 30564
- name: yob2003
num_bytes: 692931
num_examples: 31185
- name: yob2004
num_bytes: 711776
num_examples: 32048
- name: yob2005
num_bytes: 723065
num_examples: 32549
- name: yob2006
num_bytes: 757620
num_examples: 34088
- name: yob2007
num_bytes: 776893
num_examples: 34961
- name: yob2008
num_bytes: 779403
num_examples: 35079
- name: yob2009
num_bytes: 771032
num_examples: 34709
- name: yob2010
num_bytes: 756717
num_examples: 34073
- name: yob2011
num_bytes: 752804
num_examples: 33908
- name: yob2012
num_bytes: 748915
num_examples: 33747
- name: yob2013
num_bytes: 738288
num_examples: 33282
- name: yob2014
num_bytes: 737219
num_examples: 33243
- name: yob2015
num_bytes: 734183
num_examples: 33121
- name: yob2016
num_bytes: 731291
num_examples: 33010
- name: yob2017
num_bytes: 721444
num_examples: 32590
- name: yob2018
num_bytes: 708657
num_examples: 32033
download_size: 232629010
dataset_size: 43393095
- config_name: new_data
features:
- name: text
dtype: string
- name: original
dtype: string
- name: labels
list:
class_label:
names:
'0': ABOUT:female
'1': ABOUT:male
'2': PARTNER:female
'3': PARTNER:male
'4': SELF:female
'5': SELF:male
- name: class_type
dtype:
class_label:
names:
'0': about
'1': partner
'2': self
- name: turker_gender
dtype:
class_label:
names:
'0': man
'1': woman
'2': nonbinary
'3': prefer not to say
'4': no answer
- name: episode_done
dtype: bool_
- name: confidence
dtype: string
splits:
- name: train
num_bytes: 369753
num_examples: 2345
download_size: 232629010
dataset_size: 369753
- config_name: funpedia
features:
- name: text
dtype: string
- name: title
dtype: string
- name: persona
dtype: string
- name: gender
dtype:
class_label:
names:
'0': gender-neutral
'1': female
'2': male
splits:
- name: train
num_bytes: 3225542
num_examples: 23897
- name: validation
num_bytes: 402205
num_examples: 2984
- name: test
num_bytes: 396417
num_examples: 2938
download_size: 232629010
dataset_size: 4024164
- config_name: image_chat
features:
- name: caption
dtype: string
- name: id
dtype: string
- name: male
dtype: bool_
- name: female
dtype: bool_
splits:
- name: train
num_bytes: 1061285
num_examples: 9997
- name: validation
num_bytes: 35868670
num_examples: 338180
- name: test
num_bytes: 530126
num_examples: 5000
download_size: 232629010
dataset_size: 37460081
- config_name: wizard
features:
- name: text
dtype: string
- name: chosen_topic
dtype: string
- name: gender
dtype:
class_label:
names:
'0': gender-neutral
'1': female
'2': male
splits:
- name: train
num_bytes: 1158785
num_examples: 10449
- name: validation
num_bytes: 57824
num_examples: 537
- name: test
num_bytes: 53126
num_examples: 470
download_size: 232629010
dataset_size: 1269735
- config_name: convai2_inferred
features:
- name: text
dtype: string
- name: binary_label
dtype:
class_label:
names:
'0': ABOUT:female
'1': ABOUT:male
- name: binary_score
dtype: float32
- name: ternary_label
dtype:
class_label:
names:
'0': ABOUT:female
'1': ABOUT:male
'2': ABOUT:gender-neutral
- name: ternary_score
dtype: float32
splits:
- name: train
num_bytes: 9853669
num_examples: 131438
- name: validation
num_bytes: 608046
num_examples: 7801
- name: test
num_bytes: 608046
num_examples: 7801
download_size: 232629010
dataset_size: 11069761
- config_name: light_inferred
features:
- name: text
dtype: string
- name: binary_label
dtype:
class_label:
names:
'0': ABOUT:female
'1': ABOUT:male
- name: binary_score
dtype: float32
- name: ternary_label
dtype:
class_label:
names:
'0': ABOUT:female
'1': ABOUT:male
'2': ABOUT:gender-neutral
- name: ternary_score
dtype: float32
splits:
- name: train
num_bytes: 10931355
num_examples: 106122
- name: validation
num_bytes: 679692
num_examples: 6362
- name: test
num_bytes: 1375745
num_examples: 12765
download_size: 232629010
dataset_size: 12986792
- config_name: opensubtitles_inferred
features:
- name: text
dtype: string
- name: binary_label
dtype:
class_label:
names:
'0': ABOUT:female
'1': ABOUT:male
- name: binary_score
dtype: float32
- name: ternary_label
dtype:
class_label:
names:
'0': ABOUT:female
'1': ABOUT:male
'2': ABOUT:gender-neutral
- name: ternary_score
dtype: float32
splits:
- name: train
num_bytes: 27966476
num_examples: 351036
- name: validation
num_bytes: 3363802
num_examples: 41957
- name: test
num_bytes: 3830528
num_examples: 49108
download_size: 232629010
dataset_size: 35160806
- config_name: yelp_inferred
features:
- name: text
dtype: string
- name: binary_label
dtype:
class_label:
names:
'0': ABOUT:female
'1': ABOUT:male
- name: binary_score
dtype: float32
splits:
- name: train
num_bytes: 260582945
num_examples: 2577862
- name: validation
num_bytes: 324349
num_examples: 4492
- name: test
num_bytes: 53887700
num_examples: 534460
download_size: 232629010
dataset_size: 314794994
config_names:
- convai2_inferred
- funpedia
- gendered_words
- image_chat
- light_inferred
- name_genders
- new_data
- opensubtitles_inferred
- wizard
- yelp_inferred
Dataset Card for Multi-Dimensional Gender Bias Classification
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: ParlAI MD Gender Project Page
- Repository: ParlAI Github MD Gender Repository
- Paper: Multi-Dimensional Gender Bias Classification
- Leaderboard: [Needs More Information]
- Point of Contact: edinan@fb.com
Dataset Summary
The Multi-Dimensional Gender Bias Classification dataset is based on 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. It contains seven large scale datasets automatically annotated for gender information (there are eight in the original project but the Wikipedia set is not included in the HuggingFace distribution), one crowdsourced evaluation benchmark of utterance-level gender rewrites, a list of gendered names, and a list of gendered words in English.
Supported Tasks and Leaderboards
text-classification-other-gender-bias: The dataset can be used to train a model for classification of various kinds of gender bias. The model performance is evaluated based on the accuracy of the predicted labels as compared to the given labels in the dataset. Dinan et al's (2020) Transformer model achieved an average of 67.13% accuracy in binary gender prediction across the ABOUT, TO, and AS tasks. See the paper for more results.
Languages
The data is in English as spoken on the various sites where the data was collected. The associated BCP-47 code en.
Dataset Structure
Data Instances
The following are examples of data instances from the various configs in the dataset. See the MD Gender Bias dataset viewer to explore more examples.
An example from the new_data config:
{'class_type': 0,
'confidence': 'certain',
'episode_done': True,
'labels': [1],
'original': 'She designed monumental Loviisa war cemetery in 1920',
'text': 'He designed monumental Lovissa War Cemetery in 1920.',
'turker_gender': 4}
An example from the funpedia config:
{'gender': 2,
'persona': 'Humorous',
'text': 'Max Landis is a comic book writer who wrote Chronicle, American Ultra, and Victor Frankestein.',
'title': 'Max Landis'}
An example from the image_chat config:
{'caption': '<start> a young girl is holding a pink umbrella in her hand <eos>',
'female': True,
'id': '2923e28b6f588aff2d469ab2cccfac57',
'male': False}
An example from the wizard config:
{'chosen_topic': 'Krav Maga',
'gender': 2,
'text': 'Hello. I hope you might enjoy or know something about Krav Maga?'}
An example from the convai2_inferred config (the other _inferred configs have the same fields, with the exception of yelp_inferred, which does not have the ternary_label or ternary_score fields):
{'binary_label': 1,
'binary_score': 0.6521999835968018,
'ternary_label': 2,
'ternary_score': 0.4496000111103058,
'text': "hi , how are you doing ? i'm getting ready to do some cheetah chasing to stay in shape ."}
An example from the gendered_words config:
{'word_feminine': 'countrywoman',
'word_masculine': 'countryman'}
An example from the name_genders config:
{'assigned_gender': 1,
'count': 7065,
'name': 'Mary'}
Data Fields
The following are the features for each of the configs.
For the new_data config:
text: the text to be classifiedoriginal: the text before reformulationlabels: alistof classification labels, with possible values includingABOUT:female,ABOUT:male,PARTNER:female,PARTNER:male,SELF:female.class_type: a classification label, with possible values includingabout(0),partner(1),self(2).turker_gender: a classification label, with possible values includingman(0),woman(1),nonbinary(2),prefer not to say(3),no answer(4).episode_done: a boolean indicating whether the conversation was completed.confidence: a string indicating the confidence of the annotator in response to the instance label being ABOUT/TO/AS a man or woman. Possible values arecertain,pretty sure, andunsure.
For the funpedia config:
text: the text to be classified.gender: a classification label, with possible values includinggender-neutral(0),female(1),male(2), indicating the gender of the person being talked about.persona: a string describing the persona assigned to the user when talking about the entity.title: a string naming the entity the text is about.
For the image_chat config:
caption: a string description of the contents of the original image.female: a boolean indicating whether the gender of the person being talked about is female, if the image contains a person.id: a string indicating the id of the image.male: a boolean indicating whether the gender of the person being talked about is male, if the image contains a person.
For the wizard config:
text: the text to be classified.chosen_topic: a string indicating the topic of the text.gender: a classification label, with possible values includinggender-neutral(0),female(1),male(2), indicating the gender of the person being talked about.
For the _inferred configurations (again, except the yelp_inferred split, which does not have the ternary_label or ternary_score fields):
text: the text to be classified.binary_label: a classification label, with possible values includingABOUT:female,ABOUT:male.binary_score: a float indicating a score between 0 and 1.ternary_label: a classification label, with possible values includingABOUT:female,ABOUT:male,ABOUT:gender-neutral.ternary_score: a float indicating a score between 0 and 1.
For the word list:
word_masculine: a string indicating the masculine version of the word.word_feminine: a string indicating the feminine version of the word.
For the gendered name list:
assigned_gender: an integer, 1 for female, 0 for male.count: an integer.name: a string of the name.
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 imageconvai2_inferred,light_inferred,opensubtitles_inferred,yelp_inferred: Data from several source datasets with ABOUT annotations inferred by a trined classifier.
| Split | M | F | N | U | Dimension |
|---|---|---|---|---|---|
| Image Chat | 39K | 15K | 154K | - | ABOUT |
| Funpedia | 19K | 3K | 1K | - | ABOUT |
| Wizard | 6K | 1K | 1K | - | ABOUT |
| Yelp | 1M | 1M | - | - | AS |
| ConvAI2 | 22K | 22K | - | 86K | AS |
| ConvAI2 | 22K | 22K | - | 86K | TO |
| OpenSub | 149K | 69K | - | 131K | AS |
| OpenSub | 95K | 45K | - | 209K | TO |
| LIGHT | 13K | 8K | - | 83K | AS |
| LIGHT | 13K | 8K | - | 83K | TO |
| ---------- | ---- | --- | ---- | ---- | --------- |
| MDGender | 384 | 401 | - | - | ABOUT |
| MDGender | 396 | 371 | - | - | AS |
| MDGender | 411 | 382 | - | - | TO |
Dataset Creation
Curation Rationale
The curators chose to annotate the existing corpora to make their classifiers reliable on all dimensions (ABOUT/TO/AS) and across multiple domains. However, none of the existing datasets cover all three dimensions at the same time, and many of the gender labels are noisy. To enable reliable evaluation, the curators collected a specialized corpus, found in the new_data config, which acts as a gold-labeled dataset for the masculine and feminine classes.
Source Data
Initial Data Collection and Normalization
For the new_data config, the curators collected conversations between two speakers. Each speaker was provided with a persona description containing gender information, then tasked with adopting that persona and having a conversation. They were also provided with small sections of a biography from Wikipedia as the conversation topic in order to encourage crowdworkers to discuss ABOUT/TO/AS gender information. To ensure there is ABOUT/TO/AS gender information contained in each utterance, the curators asked a second set of annotators to rewrite each utterance to make it very clear that they are speaking ABOUT a man or a woman, speaking AS a man or a woman, and speaking TO a man or a woman.
Who are the source language producers?
This dataset was collected from crowdworkers from Amazon’s Mechanical Turk. All workers are English-speaking and located in the United States.
| Reported Gender | Percent of Total |
|---|---|
| Man | 67.38 |
| Woman | 18.34 |
| Non-binary | 0.21 |
| Prefer not to say | 14.07 |
Annotations
Annotation process
For the new_data config, annotators were asked to label how confident they are that someone else could predict the given gender label, allowing for flexibility between explicit genderedness (like the use of "he" or "she") and statistical genderedness.
Many of the annotated datasets contain cases where the ABOUT, AS, TO labels are not provided (i.e. unknown). In such instances, the curators apply one of two strategies. They apply the imputation strategy for data for which the ABOUT label is unknown using a classifier trained only on other Wikipedia data for which this label is provided. Data without a TO or AS label was assigned one at random, choosing between masculine and feminine with equal probability. Details of how each of the eight training datasets was annotated are as follows:
Wikipedia- to annotate ABOUT, the curators used a Wikipedia dump and extract biography pages using named entity recognition. They labeled pages with a gender based on the number of gendered pronouns (he vs. she vs. they) and labeled each paragraph in the page with this label for the ABOUT dimension.
Funpedia- Funpedia (Miller et al., 2017) contains rephrased Wikipedia sentences in a more conversational way. The curators retained only biography related sentences and annotate similar to Wikipedia, to give ABOUT labels.
Wizard of Wikipedia- Wizard of Wikipedia contains two people discussing a topic in Wikipedia. The curators retain only the conversations on Wikipedia biographies and annotate to create ABOUT labels.
ImageChat- ImageChat contains conversations discussing the contents of an image. The curators used the Xu et al. image captioning system to identify the contents of an image and select gendered examples.
Yelp- The curators used the Yelp reviewer gender predictor developed by (Subramanian et al., 2018) and retain reviews for which the classifier is very confident – this creates labels for the content creator of the review (AS). They impute ABOUT labels on this dataset using a classifier trained on the datasets 1-4.
ConvAI2- ConvAI2 contains persona-based conversations. Many personas contain sentences such as 'I am a old woman' or 'My name is Bob' which allows annotators to annotate the gender of the speaker (AS) and addressee (TO) with some confidence. Many of the personas have unknown gender. The curators impute ABOUT labels on this dataset using a classifier trained on the datasets 1-4.
OpenSubtitles- OpenSubtitles contains subtitles for movies in different languages. The curators retained English subtitles that contain a character name or identity. They annotated the character’s gender using gender kinship terms such as daughter and gender probability distribution calculated by counting the masculine and feminine names of baby names in the United States. Using the character’s gender, they produced labels for the AS dimension. They produced labels for the TO dimension by taking the gender of the next character to speak if there is another utterance in the conversation; otherwise, they take the gender of the last character to speak. They impute ABOUT labels on this dataset using a classifier trained on the datasets 1-4.
LIGHT- LIGHT contains persona-based conversation. Similarly to ConvAI2, annotators labeled the gender of each persona, giving labels for the speaker (AS) and speaking partner (TO). The curators impute ABOUT labels on this dataset using a classifier trained on the datasets 1-4.
Who are the annotators?
This dataset was annotated by crowdworkers from Amazon’s Mechanical Turk. All workers are English-speaking and located in the United States.
Personal and Sensitive Information
For privacy reasons the curators did not associate the self-reported gender of the annotator with the labeled examples in the dataset and only report these statistics in aggregate.
Considerations for Using the Data
Social Impact of Dataset
This dataset is intended for applications such as controlling for gender bias in generative models, detecting gender bias in arbitrary text, and classifying text as offensive based on its genderedness.
Discussion of Biases
Over two thirds of annotators identified as men, which may introduce biases into the dataset.
Wikipedia is also well known to have gender bias in equity of biographical coverage and lexical bias in noun references to women (see the paper's appendix for citations).
Other Known Limitations
The limitations of the Multi-Dimensional Gender Bias Classification dataset have not yet been investigated, but the curators acknowledge that more work is required to address the intersectionality of gender identities, i.e., when gender non-additively interacts with other identity characteristics. The curators point out that negative gender stereotyping is known to be alternatively weakened or reinforced by the presence of social attributes like dialect, class and race and that these differences have been found to affect gender classification in images and sentences encoders. See the paper for references.
Additional Information
Dataset Curators
Emily Dinan, Angela Fan, Ledell Wu, Jason Weston, Douwe Kiela, and Adina Williams at Facebook AI Research. Angela Fan is also affiliated with Laboratoire Lorrain d’Informatique et Applications (LORIA).
Licensing Information
The Multi-Dimensional Gender Bias Classification dataset is licensed under the MIT License.
Citation Information
@inproceedings{dinan-etal-2020-multi,
title = "Multi-Dimensional Gender Bias Classification",
author = "Dinan, Emily and
Fan, Angela and
Wu, Ledell and
Weston, Jason and
Kiela, Douwe and
Williams, Adina",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.23",
doi = "10.18653/v1/2020.emnlp-main.23",
pages = "314--331",
abstract = "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 novel, 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 new, crowdsourced evaluation benchmark. Distinguishing between gender bias along multiple dimensions enables us to train better and more fine-grained gender bias classifiers. We show our classifiers are valuable for a variety of applications, like controlling for gender bias in generative models, detecting gender bias in arbitrary text, and classifying text as offensive based on its genderedness.",
}
Contributions
Thanks to @yjernite and @mcmillanmajorafor adding this dataset.