Recently, ChatGPT has attracted great attention from both industry and academia due to its surprising abilities in natural language understanding and generation. We are particularly curious about whether it can achieve promising performance on one of the most complex tasks in aspect-based sentiment analysis, i.e., extracting aspect-category-opinion-sentiment quadruples from texts. To this end, in this paper we develop a specialized prompt template that enables ChatGPT to effectively tackle this complex quadruple extraction task. Further, we propose a selection method on few-shot examples to fully exploit the in-context learning ability of ChatGPT and uplift its effectiveness on this complex task. Finally, we provide a comparative evaluation on ChatGPT against existing state-of-the-art quadruple extraction models based on four public datasets and highlight some important findings regarding the capability boundaries of ChatGPT in the quadruple extraction.

翻译：近期，ChatGPT因其在自然语言理解与生成方面的惊人能力而引起了工业界和学术界的广泛关注。我们特别关注它能否在方面级情感分析中最复杂的任务之一——从文本中提取方面-类别-观点-情感四元组上取得良好表现。为此，本文开发了一种专门的提示模板，使ChatGPT能够有效处理这一复杂的四元组提取任务。进一步，我们提出了一种基于少样本示例的选择方法，以充分发掘ChatGPT的上下文学习能力，并提升其在该复杂任务上的有效性。最后，我们基于四个公开数据集，将ChatGPT与现有最先进的四元组提取模型进行了比较评估，并揭示了关于ChatGPT在四元组提取中能力边界的一些重要发现。