Aspect-Based Sentiment Analysis (ABSA) stands as a crucial task in predicting
the sentiment polarity associated with identified aspects within text. However,
a notable challenge in ABSA lies in precisely determining the aspects'
boundaries (start and end indices), especially for long ones, due to users'
colloquial expressions. We propose DiffusionABSA, a novel diffusion model
tailored for ABSA, which extracts the aspects progressively step by step.
Particularly, DiffusionABSA gradually adds noise to the aspect terms in the
training process, subsequently learning a denoising process that progressively
restores these terms in a reverse manner. To estimate the boundaries, we design
a denoising neural network enhanced by a syntax-aware temporal attention
mechanism to chronologically capture the interplay between aspects and
surrounding text. Empirical evaluations conducted on eight benchmark datasets
underscore the compelling advantages offered by DiffusionABSA when compared
against robust baseline models. Our code is publicly available at
this https URL

DiffusionABSA 是专门为 Aspect-Based Sentiment Analysis（ABSA）而设计的一种新型扩散模型，通过逐步提取方面术语，并采用一种逆向的去噪过程来精确确定边界，其中使用了语法感知的时间注意机制，实验证明 DiffusionABSA 在八个基准数据集上相对于其他基准模型具有明显的优势。