Vector-Quantized Prompt Learning for Paraphrase Generation
This work addresses the problem of paraphrase generation for natural language processing applications, offering a novel method that improves performance but is incremental in its approach.
The paper tackles the challenge of generating diverse and high-quality paraphrases by using instance-dependent prompts with pre-trained models, achieving new state-of-the-art results on Quora, Wikianswers, and MSCOCO datasets.
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.