  In this paper, we investigate the diversity aspect of paraphrase generation.
Prior deep learning models employ either decoding methods or add random input
noise for varying outputs. We propose a simple method Diverse Paraphrase
Generation (D-PAGE), which extends neural machine translation (NMT) models to
support the generation of diverse paraphrases with implicit rewriting patterns.
Our experimental results on two real-world benchmark datasets demonstrate that
our model generates at least one order of magnitude more diverse outputs than
the baselines in terms of a new evaluation metric Jeffrey's Divergence. We have
also conducted extensive experiments to understand various properties of our
model with a focus on diversity.
