Abstract: Aspect sentiment triplet extraction (ASTE) is a challenging subtask within aspect-based sentiment analysis. It aims to extract triplets consisting of aspect terms, opinion terms, and sentiment polarities from texts. In the recent past, generative extraction techniques have demonstrated remarkable efficacy through the sequential concatenation of target triplets, thereby enabling the autoregressive generation of triplets. However, this concatenation method may lead to sequential dependencies among unrelated triplets, introducing error accumulation during decoding. To address this issue, we propose a term-prompted and dual-path text generation (TePDuP) method. This method first utilizes machine reading comprehension (MRC) to extract aspect and opinion term in parallel, and then uses them as prompt prefixes to guide conditional triplet generation, forming a dual-path text generation framework. Meanwhile, during the training phase, we incorporate scheduled sampling as a corrective measure to mitigate the bias stemming from MRC extraction. Furthermore, in order to enhance performance to an even greater extent, we incorporate generation probabilities to merge outcomes guided by aspect and opinion terms, thereby augmenting the resilience of the model. Experimental results on the ASTE-DATA-V2 dataset show that the proposed method is effective and significantly outperforms other baseline models, and provide case studies to demonstrate that the method solves the aforementioned problem to some extent.