{
 "id": "CreativeLang/scope_simile_generation",
 "owner": "CreativeLang",
 "name": "scope_simile_generation",
 "downloads": 40,
 "likes": 2,
 "last_modified": "2023-07-06T20:57:33.000Z",
 "stated_licence": "cc-by-2.0",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [],
 "citation": "@inproceedings{chakrabarty-etal-2020-generating,\n  title = \"Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation\",\n  author = \"Chakrabarty, Tuhin  and\n    Muresan, Smaranda  and\n    Peng, Nanyun\",\n  booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n  month = nov,\n  year = \"2020\",\n  address = \"Online\",\n  publisher = \"Association for Computational Linguistics\",\n  url = \"https://www.aclweb.org/anthology/2020.emnlp-main.524\",\n  pages = \"6455--6469\",\n  abstract = \"Literary tropes, from poetry to stories, are at the crux of human imagination and communication. Figurative language such as a simile                               go beyond plain expressions to give readers new insights and inspirations. In this paper, we tackle the problem of simile generation.                               Generating a simile requires proper understanding for effective mapping of properties between two concepts. To this end, we first                                   propose a method to automatically construct a parallel corpus by transforming a large number of similes collected from Reddit to their                             literal counterpart using structured common sense knowledge. We then propose to fine-tune a pre-trained sequence to sequence model,                                 BART (Lewis et al 2019), on the literal-simile pairs to gain generalizability, so that we can generate novel similes g",
 "signal": "permissive",
 "reason": "The declared licence (cc-by-2.0) is classified as permissive.",
 "revision": "c1908138ec05246a55dd97340ee3b8ed05ecca32",
 "stated_evidence": "https://huggingface.co/datasets/CreativeLang/scope_simile_generation/blob/c1908138ec05246a55dd97340ee3b8ed05ecca32/README.md",
 "file_evidence": "https://huggingface.co/datasets/CreativeLang/scope_simile_generation/tree/c1908138ec05246a55dd97340ee3b8ed05ecca32",
 "card_nc": false
}