Google Research

An End-to-End Generative Architecture for Paraphrase Generation

  • Qian Yang
  • Zhouyuan Huo
  • Dinghan Shen
  • Yong Cheng
  • Wenlin Wang
  • Guoyin Wang
  • Lawrence Carin
EMNLP (2019)

Abstract

Generating high-quality paraphrases is a fundamental yet challenging natural language processing task. Despite the effectiveness of previous work based on generative models, there remain problems with exposure bias in recurrent neural networks, and often a failure to generate realistic sentences. To overcome these challenges, we propose the first end-to-end conditional generative architecture for generating paraphrases via adversarial training, which does not depend on extra linguistic information. Extensive experiments on four public datasets demonstrate the proposed method achieves state-of-the-art results, outperforming previous generative architectures on both automatic metrics (BLEU, METEOR, and TER) and human evaluations.

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