有时简单即有效：一项关于提升方面级情感分析性能的研究
It is Simple Sometimes: A Study On Improving Aspect-Based Sentiment Analysis Performance
AI总结：
本文提出PFInstruct，通过在指令微调的任务描述后附加NLP任务前缀，以简单方法提升方面级情感分析性能，在多个SemEval子任务上超越SOTA，并验证了前缀增强提示的有效性。
摘要：
方面级情感分析（ABSA）涉及通过多个互补的子任务，从文本数据中提取关于特定实体及其对应方面的观点。此前多项研究专注于为这些子任务设计复杂度各异的专用方案。在本文中，我们提出了一个可扩展至任意ABSA子任务的生成式框架。我们基于Scaria等人（2023）提出的指令微调模型，该模型在ABSA子任务上采用任务描述后接上下文示例的指令式方法。我们提出PFInstruct，这是对该指令学习范式的扩展，通过在任务描述后附加一个与NLP相关的任务前缀。这一简单方法在所有测试的SemEval子任务上均带来了性能提升，在ATE子任务（Rest14）上比先前的最先进（SOTA）结果提升了+3.28 F1分数，在AOOE子任务上跨SemEval数据集平均提升了+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，发现即使是带噪声的前缀也能相较于基线提升模型性能。我们的方法还在生物医学领域数据集（ERSA）上取得了具有竞争力的结果。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
