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 pretrained sequence to sequence model, BART~\cite{lewis2019bart}, on the literal-simile pairs to gain generalizability, so that we can 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 \textit{37\%}\footnote{We average 32.6\% and 41.3\% for 2 humans.} of the times, and three baseline systems including a recent metaphor generation model \textit{71\%}\footnote{We average 82\% ,63\% and 68\% for three baselines.} of the times when compared pairwise.\footnote{The simile in the title is generated by our best model. Input: Generating similes effortlessly, output: Generating similes \textit{like a Pro}.} 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.

本文提出了一种自动构建平行语料库、用预训练的序列转序列模型fine-tune的方法，能够生成不同于训练数据中的新奇的明喻，而且相比两位文学专家和三种对比方法，能够更好地生成新奇的明喻，并展示了用生成的明喻替换机器生成的故事中的文字部分可以提高语言的唤起性和被人类接受程度。

轻松生成亚瑟式的比喻：基于风格转换的比喻生成方法