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

Homepage:
Repository: https://github.com/mskandalis/rte3-french
Paper:
Leaderboard:
Point of Contact:

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
en	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:

Maximos Skandalis, Richard Moot, Christian Retoré, and Simon Robillard. 2024. New datasets for automatic detection of textual entailment and of contradictions between sentences in French. 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Turin, Italy. European Language Resources Association (ELRA) and International Committee on Computational Linguistics (ICCL).

And

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.