DiffuSent: Towards a Unified Diffusion Framework for Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA) encompasses seven distinct subtasks, each focusing on different extracted elements. Despite the proven success of generative models in unified aspect sentiment analysis, existing approaches often rely on auto-regressive token-by-token generation without grasping the whole information of the aspect and opinion terms, resulting in boundary insensitivity, particularly in context of multi-word aspect and opinion terms. To address these issues, we present DiffuSent, a non-auto-regressive diffusion framework that systematically formulates all ABSA subtasks as boundary denoising diffusion processes, progressively refining boundaries over noisy states. Furth...
| Authors | Shu Long, Yanglei Gan, Xuchuan Zhou |
|---|---|
| Primary category | cs.CL |
| Published | 31 May 2026 |
| Last updated at source | 28 August 2026 |
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