{
 "id": "LT3/different_definitions_annotations",
 "owner": "LT3",
 "name": "different_definitions_annotations",
 "downloads": 23,
 "likes": 0,
 "last_modified": "2025-08-08T14:20:04.000Z",
 "stated_licence": "mit",
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 "citation": "If you would like to use or cite this dataset, feel free to use the following BibTeX code:\n\n```bibtex\n@inproceedings{evgrafova-etal-2025-stance,\n    title = \"Stance-aware Definition Generation for Argumentative Texts\",\n    author = \"Evgrafova, Natalia  and\n      De Langhe, Loic\nand\n Hoste, Veronique and\n      Lefever, Els  \",\n    editor = \"Chistova, Elena  and\n      Cimiano, Philipp  and\n      Haddadan, Shohreh  and\n      Lapesa, Gabriella  and\n      Ruiz-Dolz, Ramon\",\n    booktitle = \"Proceedings of the 12th Argument mining Workshop\",\n    month = jul,\n    year = \"2025\",\n    address = \"Vienna, Austria\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2025.argmining-1.16/\",\n    doi = \"10.18653/v1/2025.argmining-1.16\",\n    pages = \"168--180\",\n    ISBN = \"979-8-89176-258-9\",\n    abstract = \"Definition generation models trained on dictionary data are generally expected to produce neutral and unbiased output while capturing the contextual nuances. However, previous studies have shown that generated definitions can inherit biases from both the underlying models and the input context. This paper examines the extent to which stance-related bias in argumentative data influences the generated definitions. In particular, we train a model on a slang-based dictionary to explore the feasibility of generating persuasive definitions that concisely reflect opposing parties' understandings of contested terms. Through this study, we provide new i",
 "signal": "permissive",
 "reason": "The declared licence (mit) is classified as permissive.",
 "revision": "1788f40b14274742868ecf661fa9cc2cb760df1c",
 "stated_evidence": "https://huggingface.co/datasets/LT3/different_definitions_annotations/blob/1788f40b14274742868ecf661fa9cc2cb760df1c/README.md",
 "file_evidence": "https://huggingface.co/datasets/LT3/different_definitions_annotations/tree/1788f40b14274742868ecf661fa9cc2cb760df1c",
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}