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 in-domain task-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.

本研究介绍了一种基于生成式方法的面向方面的情感分析（ABSA）的新方法，采用了两步架构的元素到元组提示（E2TP）。通过将任务拆分为可管理的部分，并使用第一步的输出作为第二步的指导，E2TP 设计了三种类型的范例，即 E2TP（饮食）、E2TP（f1）和 E2TP（f2），并在不同领域的实验中展示了其有效性和泛化能力，通过对各种基准的全面分析，表明 E2TP 在几乎所有情况下都达到了新的最先进的结果。