重思ASTE：一种结合对比学习的极简标注方案
Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning
AI总结：
针对现有方面情感三元组抽取（ASTE）方法结构复杂的问题，提出结合对比学习的极简标注方案，性能比肩或优于现有最优方法，且在少样本场景下效果超过GPT 3.5、GPT 4，为LLM范式下ASTE发展提供参考。
摘要：
方面情感三元组抽取（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抽取结构化的情感三元组。现有的ASTE方法往往通过额外结构或外部数据使任务复杂化。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来缓解这些问题。所提方法与当前最优技术相比表现相当或更优，同时设计更紧凑，计算开销更低。值得注意的是，即便在大语言模型（Large Language Models, LLMs）时代，我们的方法在少样本学习场景下仍展现出优于GPT 3.5和GPT 4的效果。本研究也为大语言模型范式下ASTE技术的发展提供了有价值的见解。
英文摘要：
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.
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