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README.md
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### Citation Information
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````BibTeX
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@inproceedings{skandalis-etal-2023-daccord,
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title = "{DACCORD} : un jeu de donn{\'e}es pour la D{\'e}tection Automatique d{'}{\'e}non{C}{\'e}s {CO}nt{R}a{D}ictoires en fran{\c{c}}ais",
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}
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````
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### Acknowledgements
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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.
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### Citation Information
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**BibTeX:**
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````BibTeX
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@inproceedings{skandalis-etal-2023-daccord,
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title = "{DACCORD} : un jeu de donn{\'e}es pour la D{\'e}tection Automatique d{'}{\'e}non{C}{\'e}s {CO}nt{R}a{D}ictoires en fran{\c{c}}ais",
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}
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````
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**ACL:**
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Maximos Skandalis, Richard Moot, and Simon Robillard. 2023. [DACCORD : un jeu de données pour la Détection Automatique d’énonCés COntRaDictoires en français](https://aclanthology.org/2023.jeptalnrecital-long.22). In *Actes de CORIA-TALN 2023. Actes de la 30e Conférence sur le Traitement Automatique des Langues Naturelles (TALN), volume 1 : travaux de recherche originaux -- articles longs*, pages 285–297, Paris, France. ATALA.
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### Acknowledgements
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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.
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