# 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.
task_categories:text-classification task_ids:multi-class-classification annotations_creators:crowdsourced annotations_creators:expert-generated language_creators:found size_categories:100K<n<1M source_datasets:DFKI-NLP/tacred language:ar language:de language:es language:fi language:fr language:hi language:hu language:ja language:pl language:ru language:tr language:zh license:other relation extraction arxiv:2305.04582 region:us
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TAGS
#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-crowdsourced #annotations_creators-expert-generated #language_creators-found #size_categories-100K<n<1M #source_datasets-DFKI-NLP/tacred #language-Arabic #language-German #language-Spanish #language-Finnish #language-French #language-Hindi #language-Hungarian #language-Japanese #language-Polish #language-Russian #language-Turkish #language-Chinese #license-other #relation extraction #arxiv-2305.04582 #region-us 

Dataset Card for "MultiTACRED"
==============================


Dataset Description
-------------------


* Homepage: URL
* Paper: MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset
* Point of Contact: See URL
* 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.
It covers 12 typologically diverse languages from 9 language families, and was created by the
Speech & Language Technology group of DFKI 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. 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 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
URL


### Supported Tasks and Leaderboards


* Tasks: Relation Classification
* Leaderboards: URL


### 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:


### 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, 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, 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.



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, which in turn
are sampled from the corpus used in the yearly
TAC Knowledge Base Population (TAC KBP) challenges.


#### Who are the source language producers?


Newswire and web texts collected for the TAC Knowledge Base Population (TAC KBP) challenges.


### 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.


### Personal and Sensitive Information


The authors 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


### Licensing Information


To respect the copyright of the underlying TACRED dataset, MultiTACRED is released via the
Linguistic Data Consortium (LDC License).
You can download MultiTACRED from the LDC MultiTACRED webpage.
If you are an LDC member, the access will be free; otherwise, an access fee of $25 is needed.


The original dataset:


For the revised version, please also cite:


For the Re-TACRED version, please also cite:


### Contributions


Thanks to @leonhardhennig for adding this dataset.



### Dataset Summary


MultiTACRED is a multilingual version of the large-scale TAC Relation Extraction Dataset.
It covers 12 typologically diverse languages from 9 language families, and was created by the
Speech & Language Technology group of DFKI 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. 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 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
URL ### Supported Tasks and Leaderboards


* Tasks: Relation Classification
* Leaderboards: URL ### 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: ### 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, 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, 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.



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, which in turn
are sampled from the corpus used in the yearly
TAC Knowledge Base Population (TAC KBP) challenges. #### Who are the source language producers?


Newswire and web texts collected for the TAC Knowledge Base Population (TAC KBP) challenges. ### 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. ### Personal and Sensitive Information


The authors 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 ### Licensing Information


To respect the copyright of the underlying TACRED dataset, MultiTACRED is released via the
Linguistic Data Consortium (LDC License).
You can download MultiTACRED from the LDC MultiTACRED webpage.
If you are an LDC member, the access will be free; otherwise, an access fee of $25 is needed.


The original dataset:


For the revised version, please also cite:


For the Re-TACRED version, please also cite: ### Contributions


Thanks to @leonhardhennig for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-crowdsourced #annotations_creators-expert-generated #language_creators-found #size_categories-100K<n<1M #source_datasets-DFKI-NLP/tacred #language-Arabic #language-German #language-Spanish #language-Finnish #language-French #language-Hindi #language-Hungarian #language-Japanese #language-Polish #language-Russian #language-Turkish #language-Chinese #license-other #relation extraction #arxiv-2305.04582 #region-us 
 ### Dataset Summary


MultiTACRED is a multilingual version of the large-scale TAC Relation Extraction Dataset.
It covers 12 typologically diverse languages from 9 language families, and was created by the
Speech & Language Technology group of DFKI 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. 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 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
URL ### Supported Tasks and Leaderboards


* Tasks: Relation Classification
* Leaderboards: URL ### 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: ### 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, 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, 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.



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, which in turn
are sampled from the corpus used in the yearly
TAC Knowledge Base Population (TAC KBP) challenges. #### Who are the source language producers?


Newswire and web texts collected for the TAC Knowledge Base Population (TAC KBP) challenges. ### 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. ### Personal and Sensitive Information


The authors 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 ### Licensing Information


To respect the copyright of the underlying TACRED dataset, MultiTACRED is released via the
Linguistic Data Consortium (LDC License).
You can download MultiTACRED from the LDC MultiTACRED webpage.
If you are an LDC member, the access will be free; otherwise, an access fee of $25 is needed.


The original dataset:


For the revised version, please also cite:


For the Re-TACRED version, please also cite: ### Contributions


Thanks to @leonhardhennig for adding this dataset.
passage: TAGS
#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-crowdsourced #annotations_creators-expert-generated #language_creators-found #size_categories-100K<n<1M #source_datasets-DFKI-NLP/tacred #language-Arabic #language-German #language-Spanish #language-Finnish #language-French #language-Hindi #language-Hungarian #language-Japanese #language-Polish #language-Russian #language-Turkish #language-Chinese #license-other #relation extraction #arxiv-2305.04582 #region-us 
 passage: ### Dataset Summary


MultiTACRED is a multilingual version of the large-scale TAC Relation Extraction Dataset.
It covers 12 typologically diverse languages from 9 language families, and was created by the
Speech & Language Technology group of DFKI 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. 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 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
URL### Supported Tasks and Leaderboards


* Tasks: Relation Classification
* Leaderboards: URL### 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:### 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, 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, a 'string' feature. passage: ### 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.



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, which in turn
are sampled from the corpus used in the yearly
TAC Knowledge Base Population (TAC KBP) challenges.#### Who are the source language producers?


Newswire and web texts collected for the TAC Knowledge Base Population (TAC KBP) challenges.### 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.### Personal and Sensitive Information


The authors 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