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Datasets:
maximoss
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rte3-multi

Tasks:
Text Classification
Modalities:
Tabular
Text
Formats:
csv
Sub-tasks:
natural-language-inference
multi-input-text-classification
Languages:
French
English
Italian
Size:
1K - 10K
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metadata
license: cc-by-4.0
language:
  - fr
  - en
  - it
  - de
task_categories:
  - text-classification
task_ids:
  - natural-language-inference
  - multi-input-text-classification
size_categories:
  - 1K<n<10K

Dataset Card for Dataset Name

Dataset Description

Dataset Summary

This repository contains all manually translated versions of RTE-3 dataset, plus the original English one. The languages into which RTE-3 dataset has so far been translated are Italian (2012), German (2013), and French (2023).

Unlike in other repositories, both our own French version and the older Italian and German ones are here annotated in 3 classes (entailment, neutral, contradiction), and not in 2 (entailment, not entailment).

If you want to use the dataset only in a specific language among those provided here, you can filter data by selecting only the language column value you wish.

Supported Tasks and Leaderboards

This dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task.

Dataset Structure

Data Fields

  • id: Index number.
  • language: The language of the concerned pair of sentences.
  • premise: The translated premise in the target language.
  • hypothesis: The translated premise in the target language.
  • label: The classification label, with possible values 0 (entailment), 1 (neutral), 2 (contradiction).
  • label_text: The classification label, with possible values entailment (0), neutral (1), contradiction (2).
  • task: The particular NLP task that the data was drawn from (IE, IR, QA and SUM).
  • length: The length of the text of the pair.

Data Splits

name development test
all_languages 3200 3200
fr 800 800
de 800 800
it 800 800

For French RTE-3:

name entailment neutral contradiction
dev 412 299 89
test 410 318 72
name short long
dev 665 135
test 683 117
name IE IR QA SUM
dev 200 200 200 200
test 200 200 200 200

Additional Information

Citation Information

BibTeX:

@inproceedings{giampiccolo-etal-2007-third,
    title = "The Third {PASCAL} Recognizing Textual Entailment Challenge",
    author = "Giampiccolo, Danilo  and
      Magnini, Bernardo  and
      Dagan, Ido  and
      Dolan, Bill",
    booktitle = "Proceedings of the {ACL}-{PASCAL} Workshop on Textual Entailment and Paraphrasing",
    month = jun,
    year = "2007",
    address = "Prague",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W07-1401",
    pages = "1--9",
}

ACL:

Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007. The Third PASCAL Recognizing Textual Entailment Challenge. In Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, pages 1–9, Prague. Association for Computational Linguistics.

Acknowledgements

This work was supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, Institut Cybersécurité Occitanie, funded by Région Occitanie, France.