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Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation

Tuhin Chakrabarty, Smaranda Muresan, and Nanyun Peng, in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020.

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Abstract

Literary tropes, from poetry to stories, are at the crux of human imagination and communication. Figurative language, such as a simile,goes beyond plain expressions to give readers new insights and inspirations. 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 fine-tune a pretrained sequence to sequence model, BART (Lewis et al., 2019),on the literal-simile pairs to generate novel similes given a literal sentence. Experiments show that our approach generates 88% novel similes that do not share properties with the training data. Human evaluation on an independent set of literal statements shows that our model generates similes better than two literary experts 37% of the times, and three baseline systems including a recent metaphor generation model 71% of the times when compared pairwise. We also show how replacing literal sentences with similes from our best model in machine generated stories improves evocativeness and leads to better acceptance by human judges.



Bib Entry

@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)},
  pages = {6455--6469},
  publisher = {Association for Computational Linguistics},
  slideslive_id = {38938962},
  year = {2020}
}

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    Full Text Slides Code Abstract BibTeX Details
    Literary tropes, from poetry to stories, are at the crux of human imagination and communication. Figurative language, such as a simile,goes beyond plain expressions to give readers new insights and inspirations. 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 fine-tune a pretrained sequence to sequence model, BART (Lewis et al., 2019),on the literal-simile pairs to generate novel similes given a literal sentence. Experiments show that our approach generates 88% novel similes that do not share properties with the training data. Human evaluation on an independent set of literal statements shows that our model generates similes better than two literary experts 37% of the times, and three baseline systems including a recent metaphor generation model 71% of the times when compared pairwise. We also show how replacing literal sentences with similes from our best model in machine generated stories improves evocativeness and leads to better acceptance by human judges.
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      booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
      pages = {6455--6469},
      publisher = {Association for Computational Linguistics},
      slideslive_id = {38938962},
      year = {2020}
    }
    
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