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We propose a method for generating paraphrases of English questions thatretain the original intent but use a different surface form. Our model combinesa careful choice of training objective with a principled informationbottleneck, to induce a latent encoding space that disentangles meaning andform. We train an encoder-decoder model to reconstruct a question from aparaphrase with the same meaning and an exemplar with the same surface form,leading to separated encoding spaces. We use a Vector-Quantized VariationalAutoencoder to represent the surface form as a set of discrete latentvariables, allowing us to use a classifier to select a different surface format test time. Crucially, our method does not require access to an externalsource of target exemplars. Extensive experiments and a human evaluation showthat we are able to generate paraphrases with a better tradeoff betweensemantic preservation and syntactic novelty compared to previous methods.

Factorising Meaning and Form for Intent-Preserving Paraphrasing