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tasksource/HYPO-L

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Permissive or share-alike

The declared licence (mit) is classified as permissive.

Declared licence: mit, from the tagEvidence: README.md at 0b26cd4
Licence files: No licence file at the top level of the repositoryEvidence: file list at 0b26cd4
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Tasks

text-classification

Citation text from the card

@inproceedings{zhang-wan-2022-mover,
    title = "{MOVER}: Mask, Over-generate and Rank for Hyperbole Generation",
    author = "Zhang, Yunxiang  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.440",
    doi = "10.18653/v1/2022.naacl-main.440",
    pages = "6018--6030",
    abstract = "Despite being a common figure of speech, hyperbole is under-researched in Figurative Language Processing. In this paper, we tackle the challenging task of hyperbole generation to transfer a literal sentence into its hyperbolic paraphrase. To address the lack of available hyperbolic sentences, we construct HYPO-XL, the first large-scale English hyperbole corpus containing 17,862 hyperbolic sentences in a non-trivial way. Based on our corpus, we propose an unsupervised method for hyperbole generation that does not require parallel literal-hyperbole pairs. During training, we fine-tune BART to infill masked hyperbolic spans of sentences from HYPO-XL. During inference, we mask part of an input literal sentence and over-generate multiple possible hyperbolic versions. Then a BERT-based ranker selects the best candidate by hyperbolicity and paraphrase quality. Automatic and human

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dataset_licence({ "id": "tasksource/HYPO-L" })
signal: "permissive"
stated_licence: "mit"
sources: 0