Hugging Face's logo

Datasets:
DFKI-SLT
/
multitacred

Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Languages:
Arabic
German
Spanish
Size:
100K<n<1M
ArXiv:
Tags:
relation extraction
License:
Dataset card Files Community
Dataset Viewer
Full Screen

The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider removing the loading script and relying on automated data support (you can use convert_to_parquet from the datasets library). If this is not possible, please open a discussion for direct help.

Dataset Card for "MultiTACRED"

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.

To use the dataset reader, you need to obtain the data from the Linguistic Data Consortium: https://catalog.ldc.upenn.edu/LDC2024T09.

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.

Supported Tasks and Leaderboards

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:

{
  "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, 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.

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, 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.

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 for adding this dataset.

Downloads last month
142
Edit dataset card Papers with Code
Homepage:
github.com
Paper:
MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset
Point of Contact:
github.com
Size of downloaded dataset files:
15.4KB (TACRED-Revisited), 3.7 MB (Re-TACRED)