Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Size:
100K<n<1M
ArXiv:
Tags:
relation extraction
License:
Update README.md
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README.md
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task_ids:
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- multi-class-classification
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paperswithcode_id: multitacred
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configs: # Optional for datasets with multiple configurations like glue.
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- original-ar
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- original-de
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- original-es
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- original-fi
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- original-fr
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- original-hi
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- original-hu
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- original-ja
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- original-pl
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- original-ru
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- original-tr
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- original-zh
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- revisited-ar
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- revisited-de
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- revisited-es
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- revisited-fi
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- revisited-fr
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- revisited-hi
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- revisited-hu
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- revisited-ja
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- revisited-pl
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- revisited-ru
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- revisited-tr
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- revisited-zh
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- retacred-ar
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- retacred-de
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- retacred-es
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- retacred-fi
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- retacred-fr
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- retacred-hi
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- retacred-hu
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- retacred-ja
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- retacred-pl
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- retacred-ru
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- retacred-tr
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- retacred-zh
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dataset_info:
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- config_name: original-ar
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features:
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## Dataset Creation
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### Curation Rationale
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### Source Data
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#### Initial Data Collection and Normalization
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#### Who are the source language producers?
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[
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### Annotations
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#### Annotation process
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See the Stanford paper, the TACRED Revisited paper, and the Re-TACRED paper, plus their appendices, for
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Translations were tokenized with language-specific Spacy models (Spacy 3.1, 'core_news/web_sm' models)
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or Trankit (Trankit 1.1.0) when there was no Spacy model for a given language (Hungarian, Turkish, Arabic, Hindi).
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#### Who are the annotators?
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### Personal and Sensitive Information
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## Considerations for Using the Data
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### Social Impact of Dataset
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### Discussion of Biases
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### Other Known Limitations
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## Additional Information
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### Dataset Curators
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### Licensing Information
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To respect the copyright of the underlying TACRED dataset, MultiTACRED is released via the
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Linguistic Data Consortium ([LDC License](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf)).
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task_ids:
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- multi-class-classification
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paperswithcode_id: multitacred
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dataset_info:
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- config_name: original-ar
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features:
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## Dataset Creation
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### Curation Rationale
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To enable more research on multilingual Relation Extraction, we generate translations of the TAC relation extraction
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dataset using DeepL and Google Translate.
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### Source Data
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#### Initial Data Collection and Normalization
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The instances of this dataset are sentences from the
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[original TACRED dataset](https://nlp.stanford.edu/projects/tacred/), which in turn
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are sampled from the [corpus](https://catalog.ldc.upenn.edu/LDC2018T03) used in the yearly
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[TAC Knowledge Base Population (TAC KBP) challenges](https://tac.nist.gov/2017/KBP/index.html).
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#### Who are the source language producers?
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Newswire and web texts collected for the [TAC Knowledge Base Population (TAC KBP) challenges](https://tac.nist.gov/2017/KBP/index.html).
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### Annotations
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#### Annotation process
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See the Stanford paper, the TACRED Revisited paper, and the Re-TACRED paper, plus their appendices, for
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Translations were tokenized with language-specific Spacy models (Spacy 3.1, 'core_news/web_sm' models)
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or Trankit (Trankit 1.1.0) when there was no Spacy model for a given language (Hungarian, Turkish, Arabic, Hindi).
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#### Who are the annotators?
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The original TACRED dataset was annotated by crowd workers, see the [TACRED paper](https://nlp.stanford.edu/pubs/zhang2017tacred.pdf).
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### Personal and Sensitive Information
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The [authors](https://nlp.stanford.edu/pubs/zhang2017tacred.pdf) of the original TACRED dataset
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have not stated measures that prevent collecting sensitive or offensive text. Therefore, we do
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not rule out the possible risk of sensitive/offensive content in the translated data.
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## Considerations for Using the Data
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### Social Impact of Dataset
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not applicable
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### Discussion of Biases
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The dataset is drawn from web and newswire text, and thus reflects any biases of these original
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texts, as well as biases introduced by the MT models.
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### Other Known Limitations
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not applicable
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## Additional Information
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### Dataset Curators
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The dataset was created by members of the
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[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)
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### Licensing Information
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To respect the copyright of the underlying TACRED dataset, MultiTACRED is released via the
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Linguistic Data Consortium ([LDC License](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf)).
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