Vector-Quantized Prompt Learning for Paraphrase Generation
Haotian Luo Yixin Liu Peidong Liu Xianggen Liu
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.