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

From Solving a Problem Boldly to Cutting the Gordian Knot: Idiomatic Text Generation