Enforcing Paraphrase Generation via Controllable Latent Diffusion

• URL: http://arxiv.org/abs/2404.08938v1
• Date: Sat, 13 Apr 2024 09:24:32 GMT
• Title: Enforcing Paraphrase Generation via Controllable Latent Diffusion
• Authors: Wei Zou, Ziyuan Zhuang, Shujian Huang, Jia Liu, Jiajun Chen,
• Abstract summary: We propose textitLatent textitDiffusion textitParaphraser(LDP), a novel paraphrase generation by modeling a controllable diffusion process. Experiments show that LDP achieves improved and diverse paraphrase generation compared to baselines.
• Score: 60.82512050963046
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Paraphrase generation aims to produce high-quality and diverse utterances of a given text. Though state-of-the-art generation via the diffusion model reconciles generation quality and diversity, textual diffusion suffers from a truncation issue that hinders efficiency and quality control. In this work, we propose \textit{L}atent \textit{D}iffusion \textit{P}araphraser~(LDP), a novel paraphrase generation by modeling a controllable diffusion process given a learned latent space. LDP achieves superior generation efficiency compared to its diffusion counterparts. It facilitates only input segments to enforce paraphrase semantics, which further improves the results without external features. Experiments show that LDP achieves improved and diverse paraphrase generation compared to baselines. Further analysis shows that our method is also helpful to other similar text generations and domain adaptations. Our code and data are available at https://github.com/NIL-zhuang/ld4pg.

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