Generative approaches have significantly influenced Aspect-Based Sentiment Analysis (ABSA), garnering considerable attention. However, existing studies often predict target text components monolithically, neglecting the benefits of utilizing single elements for tuple prediction. In this paper, we introduce Element to Tuple Prompting (E2TP), employing a two-step architecture. The former step focuses on predicting single elements, while the latter step completes the process by mapping these predicted elements to their corresponding tuples. E2TP is inspired by human problem-solving, breaking down tasks into manageable parts, using the first step's output as a guide in the second step. Within this strategy, three types of paradigms, namely E2TP($diet$), E2TP($f_1$), and E2TP($f_2$), are designed to facilitate the training process. Beyond dataset-specific experiments, our paper addresses cross-domain scenarios, demonstrating the effectiveness and generalizability of the approach. By conducting a comprehensive analysis on various benchmarks, we show that E2TP achieves new state-of-the-art results in nearly all cases.

翻译：生成式方法显著影响了基于方面的情感分析（ABSA），引起了广泛关注。然而，现有研究通常以整体方式预测目标文本组件，忽略了利用单一元素进行元组预测的益处。本文提出了元素到元组提示（E2TP），采用一种两阶段架构。前一阶段专注于预测单一元素，后一阶段则通过将这些预测元素映射到相应元组来完成整个过程。E2TP受到人类问题解决方式的启发，将任务分解为可管理的部分，并利用第一阶段的输出作为第二阶段的指导。在此策略下，设计了三种范式，即E2TP($diet$)、E2TP($f_1$)和E2TP($f_2$)，以促进训练过程。除了特定数据集的实验外，本文还探讨了跨领域场景，验证了该方法的效果和泛化能力。通过在多个基准上进行全面分析，我们展示了E2TP在几乎所有情况下均达到了新的最佳结果。