Datasets:
Tasks:
Token Classification
Sub-tasks:
coreference-resolution
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
metadata
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- coreference-resolution
paperswithcode_id: winobias
pretty_name: WinoBias
dataset_info:
- config_name: type1_anti
features:
- name: document_id
dtype: string
- name: part_number
dtype: string
- name: word_number
sequence: int32
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': '"'
'1': ''''''
'2': '#'
'3': $
'4': (
'5': )
'6': ','
'7': .
'8': ':'
'9': '``'
'10': CC
'11': CD
'12': DT
'13': EX
'14': FW
'15': IN
'16': JJ
'17': JJR
'18': JJS
'19': LS
'20': MD
'21': NN
'22': NNP
'23': NNPS
'24': NNS
'25': NN|SYM
'26': PDT
'27': POS
'28': PRP
'29': PRP$
'30': RB
'31': RBR
'32': RBS
'33': RP
'34': SYM
'35': TO
'36': UH
'37': VB
'38': VBD
'39': VBG
'40': VBN
'41': VBP
'42': VBZ
'43': WDT
'44': WP
'45': WP$
'46': WRB
'47': HYPH
'48': XX
'49': NFP
'50': AFX
'51': ADD
'52': '-LRB-'
'53': '-RRB-'
'54': '-'
- name: parse_bit
sequence: string
- name: predicate_lemma
sequence: string
- name: predicate_framenet_id
sequence: string
- name: word_sense
sequence: string
- name: speaker
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': B-PERSON
'1': I-PERSON
'2': B-NORP
'3': I-NORP
'4': B-FAC
'5': I-FAC
'6': B-ORG
'7': I-ORG
'8': B-GPE
'9': I-GPE
'10': B-LOC
'11': I-LOC
'12': B-PRODUCT
'13': I-PRODUCT
'14': B-EVENT
'15': I-EVENT
'16': B-WORK_OF_ART
'17': I-WORK_OF_ART
'18': B-LAW
'19': I-LAW
'20': B-LANGUAGE
'21': I-LANGUAGE
'22': B-DATE
'23': I-DATE
'24': B-TIME
'25': I-TIME
'26': B-PERCENT
'27': I-PERCENT
'28': B-MONEY
'29': I-MONEY
'30': B-QUANTITY
'31': I-QUANTITY
'32': B-ORDINAL
'33': I-ORDINAL
'34': B-CARDINAL
'35': I-CARDINAL
'36': '*'
'37': '0'
'38': '-'
- name: verbal_predicates
sequence: string
- name: coreference_clusters
sequence: string
splits:
- name: validation
num_bytes: 380510
num_examples: 396
- name: test
num_bytes: 402893
num_examples: 396
download_size: 65383
dataset_size: 783403
- config_name: type1_pro
features:
- name: document_id
dtype: string
- name: part_number
dtype: string
- name: word_number
sequence: int32
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': '"'
'1': ''''''
'2': '#'
'3': $
'4': (
'5': )
'6': ','
'7': .
'8': ':'
'9': '``'
'10': CC
'11': CD
'12': DT
'13': EX
'14': FW
'15': IN
'16': JJ
'17': JJR
'18': JJS
'19': LS
'20': MD
'21': NN
'22': NNP
'23': NNPS
'24': NNS
'25': NN|SYM
'26': PDT
'27': POS
'28': PRP
'29': PRP$
'30': RB
'31': RBR
'32': RBS
'33': RP
'34': SYM
'35': TO
'36': UH
'37': VB
'38': VBD
'39': VBG
'40': VBN
'41': VBP
'42': VBZ
'43': WDT
'44': WP
'45': WP$
'46': WRB
'47': HYPH
'48': XX
'49': NFP
'50': AFX
'51': ADD
'52': '-LRB-'
'53': '-RRB-'
'54': '-'
- name: parse_bit
sequence: string
- name: predicate_lemma
sequence: string
- name: predicate_framenet_id
sequence: string
- name: word_sense
sequence: string
- name: speaker
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': B-PERSON
'1': I-PERSON
'2': B-NORP
'3': I-NORP
'4': B-FAC
'5': I-FAC
'6': B-ORG
'7': I-ORG
'8': B-GPE
'9': I-GPE
'10': B-LOC
'11': I-LOC
'12': B-PRODUCT
'13': I-PRODUCT
'14': B-EVENT
'15': I-EVENT
'16': B-WORK_OF_ART
'17': I-WORK_OF_ART
'18': B-LAW
'19': I-LAW
'20': B-LANGUAGE
'21': I-LANGUAGE
'22': B-DATE
'23': I-DATE
'24': B-TIME
'25': I-TIME
'26': B-PERCENT
'27': I-PERCENT
'28': B-MONEY
'29': I-MONEY
'30': B-QUANTITY
'31': I-QUANTITY
'32': B-ORDINAL
'33': I-ORDINAL
'34': B-CARDINAL
'35': I-CARDINAL
'36': '*'
'37': '0'
'38': '-'
- name: verbal_predicates
sequence: string
- name: coreference_clusters
sequence: string
splits:
- name: validation
num_bytes: 379044
num_examples: 396
- name: test
num_bytes: 401705
num_examples: 396
download_size: 65516
dataset_size: 780749
- config_name: type2_anti
features:
- name: document_id
dtype: string
- name: part_number
dtype: string
- name: word_number
sequence: int32
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': '"'
'1': ''''''
'2': '#'
'3': $
'4': (
'5': )
'6': ','
'7': .
'8': ':'
'9': '``'
'10': CC
'11': CD
'12': DT
'13': EX
'14': FW
'15': IN
'16': JJ
'17': JJR
'18': JJS
'19': LS
'20': MD
'21': NN
'22': NNP
'23': NNPS
'24': NNS
'25': NN|SYM
'26': PDT
'27': POS
'28': PRP
'29': PRP$
'30': RB
'31': RBR
'32': RBS
'33': RP
'34': SYM
'35': TO
'36': UH
'37': VB
'38': VBD
'39': VBG
'40': VBN
'41': VBP
'42': VBZ
'43': WDT
'44': WP
'45': WP$
'46': WRB
'47': HYPH
'48': XX
'49': NFP
'50': AFX
'51': ADD
'52': '-LRB-'
'53': '-RRB-'
'54': '-'
- name: parse_bit
sequence: string
- name: predicate_lemma
sequence: string
- name: predicate_framenet_id
sequence: string
- name: word_sense
sequence: string
- name: speaker
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': B-PERSON
'1': I-PERSON
'2': B-NORP
'3': I-NORP
'4': B-FAC
'5': I-FAC
'6': B-ORG
'7': I-ORG
'8': B-GPE
'9': I-GPE
'10': B-LOC
'11': I-LOC
'12': B-PRODUCT
'13': I-PRODUCT
'14': B-EVENT
'15': I-EVENT
'16': B-WORK_OF_ART
'17': I-WORK_OF_ART
'18': B-LAW
'19': I-LAW
'20': B-LANGUAGE
'21': I-LANGUAGE
'22': B-DATE
'23': I-DATE
'24': B-TIME
'25': I-TIME
'26': B-PERCENT
'27': I-PERCENT
'28': B-MONEY
'29': I-MONEY
'30': B-QUANTITY
'31': I-QUANTITY
'32': B-ORDINAL
'33': I-ORDINAL
'34': B-CARDINAL
'35': I-CARDINAL
'36': '*'
'37': '0'
'38': '-'
- name: verbal_predicates
sequence: string
- name: coreference_clusters
sequence: string
splits:
- name: validation
num_bytes: 368421
num_examples: 396
- name: test
num_bytes: 376926
num_examples: 396
download_size: 62555
dataset_size: 745347
- config_name: type2_pro
features:
- name: document_id
dtype: string
- name: part_number
dtype: string
- name: word_number
sequence: int32
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': '"'
'1': ''''''
'2': '#'
'3': $
'4': (
'5': )
'6': ','
'7': .
'8': ':'
'9': '``'
'10': CC
'11': CD
'12': DT
'13': EX
'14': FW
'15': IN
'16': JJ
'17': JJR
'18': JJS
'19': LS
'20': MD
'21': NN
'22': NNP
'23': NNPS
'24': NNS
'25': NN|SYM
'26': PDT
'27': POS
'28': PRP
'29': PRP$
'30': RB
'31': RBR
'32': RBS
'33': RP
'34': SYM
'35': TO
'36': UH
'37': VB
'38': VBD
'39': VBG
'40': VBN
'41': VBP
'42': VBZ
'43': WDT
'44': WP
'45': WP$
'46': WRB
'47': HYPH
'48': XX
'49': NFP
'50': AFX
'51': ADD
'52': '-LRB-'
'53': '-RRB-'
'54': '-'
- name: parse_bit
sequence: string
- name: predicate_lemma
sequence: string
- name: predicate_framenet_id
sequence: string
- name: word_sense
sequence: string
- name: speaker
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': B-PERSON
'1': I-PERSON
'2': B-NORP
'3': I-NORP
'4': B-FAC
'5': I-FAC
'6': B-ORG
'7': I-ORG
'8': B-GPE
'9': I-GPE
'10': B-LOC
'11': I-LOC
'12': B-PRODUCT
'13': I-PRODUCT
'14': B-EVENT
'15': I-EVENT
'16': B-WORK_OF_ART
'17': I-WORK_OF_ART
'18': B-LAW
'19': I-LAW
'20': B-LANGUAGE
'21': I-LANGUAGE
'22': B-DATE
'23': I-DATE
'24': B-TIME
'25': I-TIME
'26': B-PERCENT
'27': I-PERCENT
'28': B-MONEY
'29': I-MONEY
'30': B-QUANTITY
'31': I-QUANTITY
'32': B-ORDINAL
'33': I-ORDINAL
'34': B-CARDINAL
'35': I-CARDINAL
'36': '*'
'37': '0'
'38': '-'
- name: verbal_predicates
sequence: string
- name: coreference_clusters
sequence: string
splits:
- name: validation
num_bytes: 366957
num_examples: 396
- name: test
num_bytes: 375144
num_examples: 396
download_size: 62483
dataset_size: 742101
- config_name: wino_bias
features:
- name: document_id
dtype: string
- name: part_number
dtype: string
- name: word_number
sequence: int32
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': '"'
'1': ''''''
'2': '#'
'3': $
'4': (
'5': )
'6': ','
'7': .
'8': ':'
'9': '``'
'10': CC
'11': CD
'12': DT
'13': EX
'14': FW
'15': IN
'16': JJ
'17': JJR
'18': JJS
'19': LS
'20': MD
'21': NN
'22': NNP
'23': NNPS
'24': NNS
'25': NN|SYM
'26': PDT
'27': POS
'28': PRP
'29': PRP$
'30': RB
'31': RBR
'32': RBS
'33': RP
'34': SYM
'35': TO
'36': UH
'37': VB
'38': VBD
'39': VBG
'40': VBN
'41': VBP
'42': VBZ
'43': WDT
'44': WP
'45': WP$
'46': WRB
'47': HYPH
'48': XX
'49': NFP
'50': AFX
'51': ADD
'52': '-LRB-'
'53': '-RRB-'
- name: parse_bit
sequence: string
- name: predicate_lemma
sequence: string
- name: predicate_framenet_id
sequence: string
- name: word_sense
sequence: string
- name: speaker
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': B-PERSON
'1': I-PERSON
'2': B-NORP
'3': I-NORP
'4': B-FAC
'5': I-FAC
'6': B-ORG
'7': I-ORG
'8': B-GPE
'9': I-GPE
'10': B-LOC
'11': I-LOC
'12': B-PRODUCT
'13': I-PRODUCT
'14': B-EVENT
'15': I-EVENT
'16': B-WORK_OF_ART
'17': I-WORK_OF_ART
'18': B-LAW
'19': I-LAW
'20': B-LANGUAGE
'21': I-LANGUAGE
'22': B-DATE
'23': I-DATE
'24': B-TIME
'25': I-TIME
'26': B-PERCENT
'27': I-PERCENT
'28': B-MONEY
'29': I-MONEY
'30': B-QUANTITY
'31': I-QUANTITY
'32': B-ORDINAL
'33': I-ORDINAL
'34': B-CARDINAL
'35': I-CARDINAL
'36': '*'
'37': '0'
- name: verbal_predicates
sequence: string
splits:
- name: train
num_bytes: 173899234
num_examples: 150335
download_size: 268725744
dataset_size: 173899234
configs:
- config_name: type1_anti
data_files:
- split: validation
path: type1_anti/validation-*
- split: test
path: type1_anti/test-*
- config_name: type1_pro
data_files:
- split: validation
path: type1_pro/validation-*
- split: test
path: type1_pro/test-*
- config_name: type2_anti
data_files:
- split: validation
path: type2_anti/validation-*
- split: test
path: type2_anti/test-*
- config_name: type2_pro
data_files:
- split: validation
path: type2_pro/validation-*
- split: test
path: type2_pro/test-*
Dataset Card for Wino_Bias dataset
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
Dataset Summary
WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias. The corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter).
Supported Tasks and Leaderboards
The underlying task is coreference resolution.
Languages
English
Dataset Structure
Data Instances
The dataset has 4 subsets: type1_pro, type1_anti, type2_pro and type2_anti.
The *_pro subsets contain sentences that reinforce gender stereotypes (e.g. mechanics are male, nurses are female), whereas the *_anti datasets contain "anti-stereotypical" sentences (e.g. mechanics are female, nurses are male).
The type1 (WB-Knowledge) subsets contain sentences for which world knowledge is necessary to resolve the co-references, and type2 (WB-Syntax) subsets require only the syntactic information present in the sentence to resolve them.
Data Fields
- document_id = This is a variation on the document filename
- part_number = Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
- word_num = This is the word index of the word in that sentence.
- tokens = This is the token as segmented/tokenized in the Treebank.
- pos_tags = This is the Penn Treebank style part of speech. When parse information is missing, all part of speeches except the one for which there is some sense or proposition annotation are marked with a XX tag. The verb is marked with just a VERB tag.
- parse_bit = This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column. When the parse information is missing, the first word of a sentence is tagged as "(TOP*" and the last word is tagged as "*)" and all intermediate words are tagged with a "*".
- predicate_lemma = The predicate lemma is mentioned for the rows for which we have semantic role information or word sense information. All other rows are marked with a "-".
- predicate_framenet_id = This is the PropBank frameset ID of the predicate in predicate_lemma.
- word_sense = This is the word sense of the word in Column tokens.
- speaker = This is the speaker or author name where available.
- ner_tags = These columns identifies the spans representing various named entities. For documents which do not have named entity annotation, each line is represented with an "*".
- verbal_predicates = There is one column each of predicate argument structure information for the predicate mentioned in predicate_lemma. If there are no predicates tagged in a sentence this is a single column with all rows marked with an "*".
Data Splits
Dev and Test Split available
Dataset Creation
Curation Rationale
The WinoBias dataset was introduced in 2018 (see paper), with its original task being coreference resolution, which is a task that aims to identify mentions that refer to the same entity or person.
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
The dataset was created by researchers familiar with the WinoBias project, based on two prototypical templates provided by the authors, in which entities interact in plausible ways.
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
"Researchers familiar with the [WinoBias] project"
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
Recent work has shown that this dataset contains grammatical issues, incorrect or ambiguous labels, and stereotype conflation, among other limitations.
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez and Kai-Wei Chan
Licensing Information
MIT Licence
Citation Information
@article{DBLP:journals/corr/abs-1804-06876, author = {Jieyu Zhao and Tianlu Wang and Mark Yatskar and Vicente Ordonez and Kai{-}Wei Chang}, title = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods}, journal = {CoRR}, volume = {abs/1804.06876}, year = {2018}, url = {http://arxiv.org/abs/1804.06876}, archivePrefix = {arXiv}, eprint = {1804.06876}, timestamp = {Mon, 13 Aug 2018 16:47:01 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1804-06876.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }
Contributions
Thanks to @akshayb7 for adding this dataset. Updated by @JieyuZhao.