  This work describes the task of metaphoric paraphrase generation, in which we
are given a literal sentence and are charged with generating a metaphoric
paraphrase. We propose two different models for this task: a lexical
replacement baseline and a novel sequence to sequence model, 'metaphor
masking', that generates free metaphoric paraphrases. We use crowdsourcing to
evaluate our results, as well as developing an automatic metric for evaluating
metaphoric paraphrases. We show that while the lexical replacement baseline is
capable of producing accurate paraphrases, they often lack metaphoricity, while
our metaphor masking model excels in generating metaphoric sentences while
performing nearly as well with regard to fluency and paraphrase quality.
