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  ### Dataset Summary
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  MultiTACRED is a multilingual version of the large-scale [https://nlp.stanford.edu/projects/tacred/](TAC Relation
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  Extraction Dataset). It covers 12 typologically diverse languages from 9 language families, and was created by the
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- Speech & Language Technology group of DFKI by machine-translating the instances of the original TACRED dataset and
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- automatically projecting their entity annotations. For details of the original TACRED's data collection and
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- annotation process, see the [https://aclanthology.org/D17-1004/](original paper). Translations are syntactically
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- validated by checking the correctness of the XML tag markup. Any translations with an invalid tag structure, e.g.
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- missing or invalid head or tail tag pairs, are discarded (on average, 2.3% of the instances).
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  Languages covered are: Arabic, Chinese, Finnish, French, German, Hindi, Hungarian, Japanese, Polish,
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  Russian, Spanish, Turkish. Intended use is supervised relation classification. Audience - researchers.
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- Please see [https://arxiv.org/abs/2305.04582](our ACL paper) for full details.
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  NOTE: This Datasetreader supports a reduced version of the original TACRED JSON format with the following changes:
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  - Removed fields: stanford_pos, stanford_ner, stanford_head, stanford_deprel, docid
@@ -4800,7 +4800,8 @@ _generate_examples()):
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  NOTE 2: The MultiTACRED dataset offers an additional 'split', namely the backtranslated test data (translated to a
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  target language and then back to English). To access this split, use dataset['backtranslated_test'].
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- You can find the TACRED dataset reader for the English version of the dataset [here](https://huggingface.co/datasets/DFKI-SLT/tacred).
 
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  ### Supported Tasks and Leaderboards
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  - **Tasks:** Relation Classification
 
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  ### Dataset Summary
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  MultiTACRED is a multilingual version of the large-scale [https://nlp.stanford.edu/projects/tacred/](TAC Relation
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  Extraction Dataset). It covers 12 typologically diverse languages from 9 language families, and was created by the
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+ [Speech & Language Technology group of DFKI](https://www.dfki.de/slt) by machine-translating the instances of the
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+ original TACRED dataset and automatically projecting their entity annotations. For details of the original TACRED's
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+ data collection and annotation process, see the [Stanford paper](https://aclanthology.org/D17-1004/). Translations are
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+ syntactically validated by checking the correctness of the XML tag markup. Any translations with an invalid tag
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+ structure, e.g. missing or invalid head or tail tag pairs, are discarded (on average, 2.3% of the instances).
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  Languages covered are: Arabic, Chinese, Finnish, French, German, Hindi, Hungarian, Japanese, Polish,
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  Russian, Spanish, Turkish. Intended use is supervised relation classification. Audience - researchers.
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+ Please see [our ACL paper](https://arxiv.org/abs/2305.04582) for full details.
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  NOTE: This Datasetreader supports a reduced version of the original TACRED JSON format with the following changes:
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  - Removed fields: stanford_pos, stanford_ner, stanford_head, stanford_deprel, docid
 
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  NOTE 2: The MultiTACRED dataset offers an additional 'split', namely the backtranslated test data (translated to a
4801
  target language and then back to English). To access this split, use dataset['backtranslated_test'].
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+ You can find the TACRED dataset reader for the English version of the dataset at
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+ [https://huggingface.co/datasets/DFKI-SLT/tacred](https://huggingface.co/datasets/DFKI-SLT/tacred).
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  ### Supported Tasks and Leaderboards
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  - **Tasks:** Relation Classification