{
 "id": "HiTZ/xnli-eu",
 "owner": "HiTZ",
 "name": "xnli-eu",
 "downloads": 301,
 "likes": 1,
 "last_modified": "2026-03-30T12:33:44.000Z",
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 "citation": "@inproceedings{heredia-etal-2024-xnlieu,\n    title = \"{XNLI}eu: a dataset for cross-lingual {NLI} in {B}asque\",\n    author = \"Heredia, Maite  and\n      Etxaniz, Julen  and\n      Zulaika, Muitze  and\n      Saralegi, Xabier  and\n      Barnes, Jeremy  and\n      Soroa, Aitor\",\n    editor = \"Duh, Kevin  and\n      Gomez, Helena  and\n      Bethard, Steven\",\n    booktitle = \"Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)\",\n    month = jun,\n    year = \"2024\",\n    address = \"Mexico City, Mexico\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2024.naacl-long.234\",\n    pages = \"4177--4188\",\n    abstract = \"XNLI is a popular Natural Language Inference (NLI) benchmark widely used to evaluate cross-lingual Natural Language Understanding (NLU) capabilities across languages. In this paper, we expand XNLI to include Basque, a low-resource language that can greatly benefit from transfer-learning approaches. The new dataset, dubbed XNLIeu, has been developed by first machine-translating the English XNLI corpus into Basque, followed by a manual post-edition step. We have conducted a series of experiments using mono- and multilingual LLMs to assess a) the effect of professional post-edition on the MT system; b) the best cross-lingual strategy for NLI in Basque; and c) whether the choice of the best cross-lingual strategy is influ",
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 "reason": "The declared licence (cc-by-nc-4.0) is classified as having use restrictions.",
 "revision": "03ecd258226287441042eed753879e101ed68909",
 "stated_evidence": "https://huggingface.co/datasets/HiTZ/xnli-eu/blob/03ecd258226287441042eed753879e101ed68909/README.md",
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}