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arXiv · 1808.04364

D-PAGE: Diverse Paraphrase Generation

Qiongkai Xu·Juyan Zhang·Lizhen Qu·Lexing Xie·Richard Nock
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Abstract

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.

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Qiongkai Xu, Juyan Zhang, Lizhen Qu, Lexing Xie, Richard Nock. 2018-08-13. D-PAGE: Diverse Paraphrase Generation. https://arxiv.org/abs/1808.04364

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