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--- |
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license: bsd-2-clause |
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task_categories: |
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- text-classification |
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task_ids: |
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- natural-language-inference |
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- multi-input-text-classification |
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language: |
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- fr |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Dataset Card for Dataset Name |
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## Dataset Description |
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- **Homepage:** |
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- **Repository:** |
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- **Paper:** |
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- **Leaderboard:** |
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- **Point of Contact:** |
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### Dataset Summary |
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This repository contains a machine-translated French version of the portion of [MultiNLI](https://cims.nyu.edu/~sbowman/multinli) concerning the 9/11 terrorist attacks (2000 examples). |
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Note that these 2000 examples included in MultiNLI (and machine translated in French here) on the subject of 9/11 are different from the 249 examples in the validation subset and the 501 ones in the test subset of XNLI on the same subject. |
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In the original subset of MultiNLI on 9/11, 26 examples were left without gold label. In this French version, we have given a gold label also to these examples (so that there are no more examples without gold label), according to our reading of the examples. |
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### Supported Tasks and Leaderboards |
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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. |
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## Dataset Structure |
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### Data Fields |
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- `premise`: The machine translated premise in the target language. |
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- `hypothesis`: The machine translated premise in the target language. |
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- `label`: The classification label, with possible values 0 (`entailment`), 1 (`neutral`), 2 (`contradiction`). |
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- `label_text`: The classification label, with possible values `entailment` (0), `neutral` (1), `contradiction` (2). |
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- `pairID`: Unique identifier for pair. |
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- `promptID`: Unique identifier for prompt. |
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- `premise_original`: The original premise from the English source dataset. |
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- `hypothesis_original`: The original hypothesis from the English source dataset. |
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### Data Splits |
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| name |entailment|neutral|contradiction| |
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|--------|---------:|------:|------------:| |
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|mnli_fr | 705 | 641 | 654 | |
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## Dataset Creation |
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The dataset was machine translated from English to French using the latest neural machine translation [opus-mt-tc-big](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-fr) model available for French. |
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The translation of the sentences was carried out on March 29th, 2023. |
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## Additional Information |
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### Citation Information |
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**BibTeX:** |
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````BibTeX |
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@InProceedings{N18-1101, |
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author = "Williams, Adina |
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and Nangia, Nikita |
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and Bowman, Samuel", |
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title = "A Broad-Coverage Challenge Corpus for |
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Sentence Understanding through Inference", |
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booktitle = "Proceedings of the 2018 Conference of |
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the North American Chapter of the |
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Association for Computational Linguistics: |
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Human Language Technologies, Volume 1 (Long |
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Papers)", |
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year = "2018", |
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publisher = "Association for Computational Linguistics", |
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pages = "1112--1122", |
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location = "New Orleans, Louisiana", |
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url = "http://aclweb.org/anthology/N18-1101" |
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} |
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```` |
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**ACL:** |
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Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://aclanthology.org/N18-1101/). In *Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*, pages 1112–1122, New Orleans, Louisiana. Association for Computational Linguistics. |
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### Acknowledgements |
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This translation of the original dataset was done as part of a research project 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. |
