Aspect-based sentiment analysis (ABSA) is the challenging task of extracting sentiment along with its corresponding aspects and opinions from human language. Due to the inherent variability of natural language, aspect and opinion terms can be expressed in various surface forms, making their accurate identification complex. Current evaluation methods for this task often restrict answers to a single ground truth, penalizing semantically equivalent predictions that differ in surface form. To address this limitation, we propose a novel, fully automated pipeline that augments existing test sets with alternative valid responses for aspect and opinion terms. This approach enables a fairer assessment of language models by accommodating linguistic diversity, resulting in higher human agreement than single-answer test sets (up to 10%p improvement in Kendall's Tau score). Our experimental results demonstrate that Large Language Models (LLMs) show substantial performance improvements over T5 models when evaluated using our augmented test set, suggesting that LLMs' capabilities in ABSA tasks may have been underestimated. This work contributes to a more comprehensive evaluation framework for ABSA, potentially leading to more accurate assessments of model performance in information extraction tasks, particularly those involving span extraction.

该研究解决了基于方面的情感分析（ABSA）评估中仅依赖单一真实值的问题，提出了一种新颖的自动化流程，通过增加替代有效响应来扩展现有测试集。这一方法提高了对语言模型的评估公平性，实验结果显示，与传统测试集相比，人类一致性有显著提高，表明大型语言模型在ABSA任务中的能力可能被低估。

单一真实值不足：在基于方面的情感分析评估中增加语言变异性