Aspect Sentiment Triplet Extraction (ASTE) is a burgeoning subtask of fine-grained sentiment analysis, aiming to extract structured sentiment triplets from unstructured textual data. Existing approaches to ASTE often complicate the task with additional structures or external data. In this research, we propose a novel tagging scheme and employ a contrastive learning approach to mitigate these challenges. The proposed approach demonstrates comparable or superior performance in comparison to state-of-the-art techniques, while featuring a more compact design and reduced computational overhead. Notably, even in the era of Large Language Models (LLMs), our method exhibits superior efficacy compared to GPT 3.5 and GPT 4 in a few-shot learning scenarios. This study also provides valuable insights for the advancement of ASTE techniques within the paradigm of large language models.

翻译：方面情感三元组提取（Aspect Sentiment Triplet Extraction, ASTE）是细粒度情感分析中一个新兴的子任务，旨在从非结构化文本数据中提取结构化情感三元组。现有ASTE方法常通过引入额外结构或外部数据使得任务复杂化。在本研究中，我们提出了一种新颖的标注方案，并采用对比学习方法以缓解这些挑战。所提方法在设计上更为紧凑、计算开销更小，同时展现出与最先进技术相当或更优的性能。值得注意的是，即便在大语言模型（LLMs）时代，我们的方法在少样本学习场景下仍表现出优于GPT 3.5和GPT 4的效能。本研究也为大语言模型范式下ASTE技术的发展提供了宝贵见解。