Let's Rectify Step by Step: Improving Aspect-based Sentiment Analysis
  with Diffusion Models
Shunyu Liu Jie Zhou Qunxi Zhu Qin Chen Qingchun Bai Jun Xiao Liang He
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
https://github.com/Qlb6x/DiffusionABSA.