  We study a new application for text generation -- idiomatic sentence
generation -- which aims to transfer literal phrases in sentences into their
idiomatic counterparts. Inspired by psycholinguistic theories of idiom use in
one's native language, we propose a novel approach for this task, which
retrieves the appropriate idiom for a given literal sentence, extracts the span
of the sentence to be replaced by the idiom, and generates the idiomatic
sentence by using a neural model to combine the retrieved idiom and the
remainder of the sentence. Experiments on a novel dataset created for this task
show that our model is able to effectively transfer literal sentences into
idiomatic ones. Furthermore, automatic and human evaluations show that for this
task, the proposed model outperforms a series of competitive baseline models
for text generation.
