Aspect-Based Sentiment Analysis (ABSA) involves extracting opinions from textual data about specific entities and their corresponding aspects through various complementary subtasks. Several prior research has focused on developing ad hoc designs of varying complexities for these subtasks. In this paper, we present a generative framework extensible to any ABSA subtask. We build upon the instruction tuned model proposed by Scaria et al. (2023), who present an instruction-based model with task descriptions followed by in-context examples on ABSA subtasks. We propose PFInstruct, an extension to this instruction learning paradigm by appending an NLP-related task prefix to the task description. This simple approach leads to improved performance across all tested SemEval subtasks, surpassing previous state-of-the-art (SOTA) on the ATE subtask (Rest14) by +3.28 F1-score, and on the AOOE subtask by an average of +5.43 F1-score across SemEval datasets. Furthermore, we explore the impact of the prefix-enhanced prompt quality on the ABSA subtasks and find that even a noisy prefix enhances model performance compared to the baseline. Our method also achieves competitive results on a biomedical domain dataset (ERSA).

翻译：基于方面的情感分析（ABSA）涉及通过多个互补子任务从文本数据中提取关于特定实体及其对应方面的观点。先前的一些研究专注于为这些子任务开发不同复杂度的特定设计。本文提出了一种可扩展至任何ABSA子任务的生成式框架。我们基于Scaria等人（2023）提出的指令调优模型进行构建，该模型通过任务描述结合ABSA子任务的上下文示例来实现基于指令的建模。我们提出了PFInstruct方法，通过向任务描述附加自然语言处理相关任务前缀来扩展该指令学习范式。这种简单方法在所有测试的SemEval子任务中均实现了性能提升，其中在ATE子任务（Rest14）上以+3.28 F1分数超越先前最优结果，在AOOE子任务上跨SemEval数据集平均提升+5.43 F1分数。此外，我们探究了前缀增强提示质量对ABSA子任务的影响，发现即使使用含噪声的前缀也能较基线提升模型性能。我们的方法在生物医学领域数据集（ERSA）上也取得了具有竞争力的结果。