Datasets:

DFKI-SLT
/

multitacred

	---
	language:
	- ar
	- de
	- es
	- fi
	- fr
	- hi
	- hu
	- ja
	- pl
	- ru
	- tr
	- zh
	license: other
	license_details: https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf
	tags:
	- relation extraction
	annotations_creators:
	- crowdsourced
	- expert-generated
	language_creators:
	- found
	pretty_name: MultiTACRED - Multilingual TAC Relation Extraction Dataset
	size_categories:
	- 100K<n<1M
	source_datasets:
	- DFKI-NLP/tacred
	task_categories: # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
	- text-classification
	task_ids:
	- multi-class-classification
	paperswithcode_id: multitacred
	dataset_info:
	- config_name: original-ar
	features:
	- name: id
	dtype: string
	- name: token
	sequence: string
	- name: subj_start
	dtype: int32
	- name: subj_end
	dtype: int32
	- name: subj_type
	dtype:
	class_label:
	names:
	'0': LOCATION
	'1': ORGANIZATION
	'2': PERSON
	'3': DATE
	'4': MONEY
	'5': PERCENT
	'6': TIME
	'7': CAUSE_OF_DEATH
	'8': CITY
	'9': COUNTRY
	'10': CRIMINAL_CHARGE
	'11': EMAIL
	'12': HANDLE
	'13': IDEOLOGY
	'14': NATIONALITY
	'15': RELIGION
	'16': STATE_OR_PROVINCE
	'17': TITLE
	'18': URL
	'19': NUMBER
	'20': ORDINAL
	'21': MISC
	'22': DURATION
	'23': O
	- name: obj_start
	dtype: int32
	- name: obj_end
	dtype: int32
	- name: obj_type
	dtype:
	class_label:
	names:
	'0': LOCATION
	'1': ORGANIZATION
	'2': PERSON
	'3': DATE
	'4': MONEY
	'5': PERCENT
	'6': TIME
	'7': CAUSE_OF_DEATH
	'8': CITY
	'9': COUNTRY
	'10': CRIMINAL_CHARGE
	'11': EMAIL
	'12': HANDLE
	'13': IDEOLOGY
	'14': NATIONALITY
	'15': RELIGION
	'16': STATE_OR_PROVINCE
	'17': TITLE
	'18': URL
	'19': NUMBER
	'20': ORDINAL
	'21': MISC
	'22': DURATION
	'23': O
	- name: relation
	dtype:
	class_label:
	names:
	'0': no_relation
	'1': org:alternate_names
	'2': org:city_of_headquarters
	'3': org:country_of_headquarters
	'4': org:dissolved
	'5': org:founded
	'6': org:founded_by
	'7': org:member_of
	'8': org:members
	'9': org:number_of_employees/members
	'10': org:parents
	'11': org:political/religious_affiliation
	'12': org:shareholders
	'13': org:stateorprovince_of_headquarters
	'14': org:subsidiaries
	'15': org:top_members/employees
	'16': org:website
	'17': per:age
	'18': per:alternate_names
	'19': per:cause_of_death
	'20': per:charges
	'21': per:children
	'22': per:cities_of_residence
	'23': per:city_of_birth
	'24': per:city_of_death
	'25': per:countries_of_residence
	'26': per:country_of_birth
	'27': per:country_of_death
	'28': per:date_of_birth
	'29': per:date_of_death
	'30': per:employee_of
	'31': per:origin
	'32': per:other_family
	'33': per:parents
	'34': per:religion
	'35': per:schools_attended
	'36': per:siblings
	'37': per:spouse
	'38': per:stateorprovince_of_birth
	'39': per:stateorprovince_of_death
	'40': per:stateorprovinces_of_residence
	'41': per:title
	splits:
	- name: train
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	num_examples: 67736
	- name: test
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	num_examples: 15425
	- name: validation
	num_bytes: 10353930
	num_examples: 22502
	- name: backtranslated_test
	num_bytes: 5687302
	num_examples: 15425
	download_size: 0
	dataset_size: 55307874
	- config_name: revisited-ar
	features:
	- name: id
	dtype: string
	- name: token
	sequence: string
	- name: subj_start
	dtype: int32
	- name: subj_end
	dtype: int32
	- name: subj_type
	dtype:
	class_label:
	names:
	'0': LOCATION
	'1': ORGANIZATION
	'2': PERSON
	'3': DATE
	'4': MONEY
	'5': PERCENT
	'6': TIME
	'7': CAUSE_OF_DEATH
	'8': CITY
	'9': COUNTRY
	'10': CRIMINAL_CHARGE
	'11': EMAIL
	'12': HANDLE
	'13': IDEOLOGY
	'14': NATIONALITY
	'15': RELIGION
	'16': STATE_OR_PROVINCE
	'17': TITLE
	'18': URL
	'19': NUMBER
	'20': ORDINAL
	'21': MISC
	'22': DURATION
	'23': O
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	dtype: int32
	- name: obj_end
	dtype: int32
	- name: obj_type
	dtype:
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	'1': ORGANIZATION
	'2': PERSON
	'3': DATE
	'4': MONEY
	'5': PERCENT
	'6': TIME
	'7': CAUSE_OF_DEATH
	'8': CITY
	'9': COUNTRY
	'10': CRIMINAL_CHARGE
	'11': EMAIL
	'12': HANDLE
	'13': IDEOLOGY
	'14': NATIONALITY
	'15': RELIGION
	'16': STATE_OR_PROVINCE
	'17': TITLE
	'18': URL
	'19': NUMBER
	'20': ORDINAL
	'21': MISC
	'22': DURATION
	'23': O
	- name: relation
	dtype:
	class_label:
	names:
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	'1': org:alternate_names
	'2': org:city_of_headquarters
	'3': org:country_of_headquarters
	'4': org:dissolved
	'5': org:founded
	'6': org:founded_by
	'7': org:member_of
	'8': org:members
	'9': org:number_of_employees/members
	'10': org:parents
	'11': org:political/religious_affiliation
	'12': org:shareholders
	'13': org:stateorprovince_of_headquarters
	'14': org:subsidiaries
	'15': org:top_members/employees
	'16': org:website
	'17': per:age
	'18': per:alternate_names
	'19': per:cause_of_death
	'20': per:charges
	'21': per:children
	'22': per:cities_of_residence
	'23': per:city_of_birth
	'24': per:city_of_death
	'25': per:countries_of_residence
	'26': per:country_of_birth
	'27': per:country_of_death
	'28': per:date_of_birth
	'29': per:date_of_death
	'30': per:employee_of
	'31': per:origin
	'32': per:other_family
	'33': per:parents
	'34': per:religion
	'35': per:schools_attended
	'36': per:siblings
	'37': per:spouse
	'38': per:stateorprovince_of_birth
	'39': per:stateorprovince_of_death
	'40': per:stateorprovinces_of_residence
	'41': per:title
	splits:
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	num_examples: 67736
	- name: test
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	num_examples: 15425
	- name: validation
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	num_examples: 22502
	- name: backtranslated_test
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	num_examples: 15425
	download_size: 157165
	dataset_size: 55307874
	- config_name: retacred-ar
	features:
	- name: id
	dtype: string
	- name: token
	sequence: string
	- name: subj_start
	dtype: int32
	- name: subj_end
	dtype: int32
	- name: subj_type
	dtype:
	class_label:
	names:
	'0': LOCATION
	'1': ORGANIZATION
	'2': PERSON
	'3': DATE
	'4': MONEY
	'5': PERCENT
	'6': TIME
	'7': CAUSE_OF_DEATH
	'8': CITY
	'9': COUNTRY
	'10': CRIMINAL_CHARGE
	'11': EMAIL
	'12': HANDLE
	'13': IDEOLOGY
	'14': NATIONALITY
	'15': RELIGION
	'16': STATE_OR_PROVINCE
	'17': TITLE
	'18': URL
	'19': NUMBER
	'20': ORDINAL
	'21': MISC
	'22': DURATION
	'23': O
	- name: obj_start
	dtype: int32
	- name: obj_end
	dtype: int32
	- name: obj_type
	dtype:
	class_label:
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	'1': ORGANIZATION
	'2': PERSON
	'3': DATE
	'4': MONEY
	'5': PERCENT
	'6': TIME
	'7': CAUSE_OF_DEATH
	'8': CITY
	'9': COUNTRY
	'10': CRIMINAL_CHARGE
	'11': EMAIL
	'12': HANDLE
	'13': IDEOLOGY
	'14': NATIONALITY
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	'16': STATE_OR_PROVINCE
	'17': TITLE
	'18': URL
	'19': NUMBER
	'20': ORDINAL
	'21': MISC
	'22': DURATION
	'23': O
	- name: relation
	dtype:
	class_label:
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	'30': per:other_family
	'31': per:parents
	'32': per:religion
	'33': per:schools_attended
	'34': per:siblings
	'35': per:spouse
	'36': per:stateorprovince_of_birth
	'37': per:stateorprovince_of_death
	'38': per:stateorprovinces_of_residence
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	- name: test
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	- name: validation
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	- name: backtranslated_test
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	dataset_size: 47575415
	- config_name: original-de
	features:
	- name: id
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	- name: token
	sequence: string
	- name: subj_start
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	- name: subj_type
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	'1': ORGANIZATION
	'2': PERSON
	'3': DATE
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	'5': PERCENT
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	'7': CAUSE_OF_DEATH
	'8': CITY
	'9': COUNTRY
	'10': CRIMINAL_CHARGE
	'11': EMAIL
	'12': HANDLE
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	'14': NATIONALITY
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	'16': STATE_OR_PROVINCE
	'17': TITLE
	'18': URL
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	'20': ORDINAL
	'21': MISC
	'22': DURATION
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	'2': PERSON
	'3': DATE
	'4': MONEY
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	'8': CITY
	'9': COUNTRY
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	'14': NATIONALITY
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	'36': per:siblings
	'37': per:spouse
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	'11': EMAIL
	'12': HANDLE
	'13': IDEOLOGY
	'14': NATIONALITY
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	'17': TITLE
	'18': URL
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	'20': ORDINAL
	'21': MISC
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	'23': O
	- name: relation
	dtype:
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	- name: validation
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	num_bytes: 4805734
	num_examples: 13352
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	- name: token
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	download_size: 0
	dataset_size: 45147519
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	features:
	- name: id
	dtype: string
	- name: token
	sequence: string
	- name: subj_start
	dtype: int32
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	'21': MISC
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	'21': MISC
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	dtype:
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	'12': org:shareholders
	'13': org:stateorprovince_of_headquarters
	'14': org:subsidiaries
	'15': org:top_members/employees
	'16': org:website
	'17': per:age
	'18': per:alternate_names
	'19': per:cause_of_death
	'20': per:charges
	'21': per:children
	'22': per:cities_of_residence
	'23': per:city_of_birth
	'24': per:city_of_death
	'25': per:countries_of_residence
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	'27': per:country_of_death
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	'30': per:employee_of
	'31': per:origin
	'32': per:other_family
	'33': per:parents
	'34': per:religion
	'35': per:schools_attended
	'36': per:siblings
	'37': per:spouse
	'38': per:stateorprovince_of_birth
	'39': per:stateorprovince_of_death
	'40': per:stateorprovinces_of_residence
	'41': per:title
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	- name: train
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	num_examples: 65260
	- name: test
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	num_examples: 14694
	- name: validation
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	features:
	- name: id
	dtype: string
	- name: token
	sequence: string
	- name: subj_start
	dtype: int32
	- name: subj_end
	dtype: int32
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	'20': ORDINAL
	'21': MISC
	'22': DURATION
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	download_size: 3702157
	dataset_size: 38800079
	---
	# Dataset Card for "MultiTACRED"

	## Dataset Description
	- Homepage: [https://github.com/DFKI-NLP/MultiTACRED](https://github.com/DFKI-NLP/MultiTACRED)
	- Paper: [MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset](https://arxiv.org/abs/2305.04582)
	- Point of Contact: See [https://github.com/DFKI-NLP/MultiTACRED](https://github.com/DFKI-NLP/MultiTACRED)
	- Size of downloaded dataset files: 15.4KB (TACRED-Revisited), 3.7 MB (Re-TACRED)
	- Size of the generated dataset: 1.7 GB (all languages, all versions)
	- Total amount of disk used: 1.7 GB (all languages, all versions)
	### Dataset Summary
	MultiTACRED is a multilingual version of the large-scale [TAC Relation Extraction Dataset](https://nlp.stanford.edu/projects/tacred).
	It covers 12 typologically diverse languages from 9 language families, and was created by the
	[Speech & Language Technology group of DFKI](https://www.dfki.de/slt) by machine-translating the instances of the
	original TACRED dataset and automatically projecting their entity annotations. For details of the original TACRED's
	data collection and annotation process, see the [Stanford paper](https://aclanthology.org/D17-1004/). Translations are
	syntactically validated by checking the correctness of the XML tag markup. Any translations with an invalid tag
	structure, e.g. missing or invalid head or tail tag pairs, are discarded (on average, 2.3% of the instances).

	Languages covered are: Arabic, Chinese, Finnish, French, German, Hindi, Hungarian, Japanese, Polish,
	Russian, Spanish, Turkish. Intended use is supervised relation classification. Audience - researchers.

	Please see our [ACL paper](https://aclanthology.org/2023.acl-long.210/) for full details.

	NOTE: This Datasetreader supports a reduced version of the original TACRED JSON format with the following changes:
	- Removed fields: stanford_pos, stanford_ner, stanford_head, stanford_deprel, docid
	The motivation for this is that we want to support additional languages, for which these fields were not required
	or available. The reader expects the specification of a language-specific configuration specifying the variant
	(original, revisited or retacred) and the language (as a two-letter iso code).

	The DatasetReader changes the offsets of the following fields, to conform with standard Python usage (see
	_generate_examples()):
	- subj_end to subj_end + 1 (make end offset exclusive)
	- obj_end to obj_end + 1 (make end offset exclusive)

	NOTE 2: The MultiTACRED dataset offers an additional 'split', namely the backtranslated test data (translated to a
	target language and then back to English). To access this split, use dataset['backtranslated_test'].

	You can find the TACRED dataset reader for the English version of the dataset at
	[https://huggingface.co/datasets/DFKI-SLT/tacred](https://huggingface.co/datasets/DFKI-SLT/tacred).

	### Supported Tasks and Leaderboards
	- Tasks: Relation Classification
	- Leaderboards: [https://paperswithcode.com/sota/relation-extraction-on-multitacred](https://paperswithcode.com/sota/relation-extraction-on-multitacred)

	### Languages
	The languages in the dataset are Arabic, German, English, Spanish, Finnish, French, Hindi, Hungarian, Japanese, Polish, Russian, Turkish, and Chinese.
	All languages except English are machine-translated using either Deepl's or Google's translation APIs.
	## Dataset Structure
	### Data Instances
	- Size of downloaded dataset files: 15.4KB (TACRED-Revisited), 3.7 MB (Re-TACRED)
	- Size of the generated dataset: 1.7 GB (all languages, all versions)
	- Total amount of disk used: 1.7 GB (all languages, all versions)

	An example of 'train' looks as follows:
	```json
	{
	"id": "61b3a5c8c9a882dcfcd2",
	"token": ["Tom", "Thabane", "trat", "im", "Oktober", "letzten", "Jahres", "zurück", ",", "um", "die", "All", "Basotho", "Convention", "-LRB-", "ABC", "-RRB-", "zu", "gründen", ",", "die", "mit", "17", "Abgeordneten", "das", "Wort", "ergriff", ",", "woraufhin", "der", "konstitutionelle", "Monarch", "König", "Letsie", "III.", "das", "Parlament", "auflöste", "und", "Neuwahlen", "ansetzte", "."],
	"relation": "org:founded_by",
	"subj_start": 11,
	"subj_end": 13,
	"obj_start": 0,
	"obj_end": 1,
	"subj_type": "ORGANIZATION",
	"obj_type": "PERSON"
	}
	```
	### Data Fields
	The data fields are the same among all splits.

	- `id`: the instance id of this sentence, a `string` feature.
	- `token`: the list of tokens of this sentence, a `list` of `string` features.
	- `relation`: the relation label of this instance, a `string` classification label.
	- `subj_start`: the 0-based index of the start token of the relation subject mention, an `ìnt` feature.
	- `subj_end`: the 0-based index of the end token of the relation subject mention, exclusive, an `ìnt` feature.
	- `subj_type`: the NER type of the subject mention, among the types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `string` feature.
	- `obj_start`: the 0-based index of the start token of the relation object mention, an `ìnt` feature.
	- `obj_end`: the 0-based index of the end token of the relation object mention, exclusive, an `ìnt` feature.
	- `obj_type`: the NER type of the object mention, among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `string` feature.
	### Data Splits
	To miminize dataset bias, TACRED is stratified across years in which the TAC KBP challenge was run.
	Languages statistics for the splits differ because not all instances could be translated with the
	subject and object entity markup still intact, these were discarded.

	\| Language \| Train \| Dev \| Test \| Backtranslated Test \| Translation Engine \|
	\| ----- \| ------ \| ----- \| ---- \| ---- \| ---- \|
	\| en \| 68,124 \| 22,631 \| 15,509 \| - \| - \|
	\| ar \| 67,736 \| 22,502 \| 15,425 \| 15,425 \| Google \|
	\| de \| 67,253 \| 22,343 \| 15,282 \| 15,079 \| DeepL \|
	\| es \| 65,247 \| 21,697 \| 14,908 \| 14,688 \| DeepL \|
	\| fi \| 66,751 \| 22,268 \| 15,083 \| 14,462 \| DeepL \|
	\| fr \| 66,856 \| 22,298 \| 15,237 \| 15,088 \| DeepL \|
	\| hi \| 67,751 \| 22,511 \| 15,440 \| 15,440 \| Google \|
	\| hu \| 67,766 \| 22,519 \| 15,436 \| 15,436 \| Google \|
	\| ja \| 61,571 \| 20,290 \| 13,701 \| 12,913 \| DeepL \|
	\| pl \| 68,124 \| 22,631 \| 15,509 \| 15,509 \| Google \|
	\| ru \| 66,413 \| 21,998 \| 14,995 \| 14,703 \| DeepL \|
	\| tr \| 67,749 \| 22,510 \| 15,429 \| 15,429 \| Google \|
	\| zh \| 65,260 \| 21,538 \| 14,694 \| 14,021 \| DeepL \|

	## Dataset Creation
	### Curation Rationale
	To enable more research on multilingual Relation Extraction, we generate translations of the TAC relation extraction
	dataset using DeepL and Google Translate.
	### Source Data
	#### Initial Data Collection and Normalization
	The instances of this dataset are sentences from the
	[original TACRED dataset](https://nlp.stanford.edu/projects/tacred/), which in turn
	are sampled from the [corpus](https://catalog.ldc.upenn.edu/LDC2018T03) used in the yearly
	[TAC Knowledge Base Population (TAC KBP) challenges](https://tac.nist.gov/2017/KBP/index.html).
	#### Who are the source language producers?
	Newswire and web texts collected for the [TAC Knowledge Base Population (TAC KBP) challenges](https://tac.nist.gov/2017/KBP/index.html).
	### Annotations
	#### Annotation process
	See the Stanford paper, the TACRED Revisited paper, and the Re-TACRED paper, plus their appendices, for
	details on the original annotation process. The translated versions do not change the original labels.

	Translations were tokenized with language-specific Spacy models (Spacy 3.1, 'core_news/web_sm' models)
	or Trankit (Trankit 1.1.0) when there was no Spacy model for a given language (Hungarian, Turkish, Arabic, Hindi).
	#### Who are the annotators?
	The original TACRED dataset was annotated by crowd workers, see the [TACRED paper](https://nlp.stanford.edu/pubs/zhang2017tacred.pdf).
	### Personal and Sensitive Information
	The [authors](https://nlp.stanford.edu/pubs/zhang2017tacred.pdf) of the original TACRED dataset
	have not stated measures that prevent collecting sensitive or offensive text. Therefore, we do
	not rule out the possible risk of sensitive/offensive content in the translated data.
	## Considerations for Using the Data
	### Social Impact of Dataset
	not applicable
	### Discussion of Biases
	The dataset is drawn from web and newswire text, and thus reflects any biases of these original
	texts, as well as biases introduced by the MT models.
	### Other Known Limitations
	not applicable
	## Additional Information
	### Dataset Curators
	The dataset was created by members of the
	[DFKI SLT team: Leonhard Hennig, Philippe Thomas, Sebastian Möller, Gabriel Kressin](https://www.dfki.de/en/web/research/research-departments/speech-and-language-technology/speech-and-language-technology-staff-members)

	### Licensing Information
	To respect the copyright of the underlying TACRED dataset, MultiTACRED is released via the
	Linguistic Data Consortium ([LDC License](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf)).
	You can download MultiTACRED from the [LDC MultiTACRED webpage](https://catalog.ldc.upenn.edu/TODO).
	If you are an LDC member, the access will be free; otherwise, an access fee of $25 is needed.
	### Citation Information
	The original dataset:
	```
	@inproceedings{zhang2017tacred,
	author = {Zhang, Yuhao and Zhong, Victor and Chen, Danqi and Angeli, Gabor and Manning, Christopher D.},
	booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)},
	title = {Position-aware Attention and Supervised Data Improve Slot Filling},
	url = {https://nlp.stanford.edu/pubs/zhang2017tacred.pdf},
	pages = {35--45},
	year = {2017}
	}
	```

	For the revised version, please also cite:
	```
	@inproceedings{alt-etal-2020-tacred,
	title = "{TACRED} Revisited: A Thorough Evaluation of the {TACRED} Relation Extraction Task",
	author = "Alt, Christoph and
	Gabryszak, Aleksandra and
	Hennig, Leonhard",
	booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
	month = jul,
	year = "2020",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/2020.acl-main.142",
	doi = "10.18653/v1/2020.acl-main.142",
	pages = "1558--1569",
	}
	```

	For the Re-TACRED version, please also cite:
	```
	@inproceedings{DBLP:conf/aaai/StoicaPP21,
	author = {George Stoica and
	Emmanouil Antonios Platanios and
	Barnab{\'{a}}s P{\'{o}}czos},
	title = {Re-TACRED: Addressing Shortcomings of the {TACRED} Dataset},
	booktitle = {Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI}
	2021, Thirty-Third Conference on Innovative Applications of Artificial
	Intelligence, {IAAI} 2021, The Eleventh Symposium on Educational Advances
	in Artificial Intelligence, {EAAI} 2021, Virtual Event, February 2-9,
	2021},
	pages = {13843--13850},
	publisher = {{AAAI} Press},
	year = {2021},
	url = {https://ojs.aaai.org/index.php/AAAI/article/view/17631},
	}
	```
	### Contributions
	Thanks to [@leonhardhennig](https://github.com/leonhardhennig) for adding this dataset.