Deep generative modeling of natural languages has achieved many successes,
such as producing fluent sentences and translating from one language into
another. However, the development of generative modeling techniques for
paraphrase generation still lags behind largely due to the challenges in
addressing the complex conflicts between expression diversity and semantic
preservation. This paper proposes to generate diverse and high-quality
paraphrases by exploiting the pre-trained models with instance-dependent
prompts. To learn generalizable prompts, we assume that the number of abstract
transforming patterns of paraphrase generation (governed by prompts) is finite
and usually not large. Therefore, we present vector-quantized prompts as the
cues to control the generation of pre-trained models. Extensive experiments
demonstrate that the proposed method achieves new state-of-art results on three
benchmark datasets, including Quora, Wikianswers, and MSCOCO. We will release
all the code upon acceptance.

通过利用预训练模型和实例相关提示，本文提出生成多样且高质量的复述词句的方法，使用向量量化的提示来控制预训练模型的生成。实验表明此方法在三个基准数据集上取得了最新的最佳结果，包括 Quora、Wikianswers 和 MSCOCO。一旦被接受，我们将公开所有代码。