CreativeLang/scope_simile_generation

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@inproceedings{chakrabarty-etal-2020-generating,
  title = "Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation",
  author = "Chakrabarty, Tuhin  and
    Muresan, Smaranda  and
    Peng, Nanyun",
  booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
  month = nov,
  year = "2020",
  address = "Online",
  publisher = "Association for Computational Linguistics",
  url = "https://www.aclweb.org/anthology/2020.emnlp-main.524",
  pages = "6455--6469",
  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

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dataset_licence({ "id": "CreativeLang/scope_simile_generation" })
signal: "permissive"
stated_licence: "cc-by-2.0"
sources: 0