  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.
