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.

翻译：基于方面的情感分析（ABSA）是一项关键任务，旨在预测文本中识别出的方面所对应的情感极性。然而，由于用户的非正式表达，ABSA面临的一个显著挑战是精确确定方面的边界（起始和结束索引），尤其是对于较长的方面。我们提出DiffusionABSA，一种专为ABSA设计的新型扩散模型，该模型逐步逐阶段提取方面。具体来说，DiffusionABSA在训练过程中逐步向方面词添加噪声，随后学习一个去噪过程，以逆序方式逐步恢复这些词。为了估计边界，我们设计了一个由语法感知的时间注意力机制增强的去噪神经网络，以按顺序捕捉方面与周围文本之间的相互作用。在八个基准数据集上进行的实证评估表明，与稳健的基线模型相比，DiffusionABSA具有显著优势。我们的代码已在https://github.com/Qlb6x/DiffusionABSA公开。