Nek Minit：利用实用元认知提示实现澳大利亚英语与印度英语的可解释讽刺检测
Nek Minit: Harnessing Pragmatic Metacognitive Prompting for Explainable Sarcasm Detection of Australian and Indian English
AI总结：
本研究针对地域英语变体讽刺检测的难点，提出利用实用元认知提示（PMP）实现可解释讽刺检测，在澳、印英语及标准英语数据集上验证了其相比其他提示策略的显著性能提升。
摘要：
讽刺是情感分析面临的一大挑战，原因在于其表述的情感与隐含情感之间存在不一致性。当这种隐含含义可能与特定国家或地理区域相关时，这一挑战会进一步加剧。实用元认知提示（Pragmatic metacognitive prompting，PMP）是一种受认知启发的技术，已被用于实用推理。在本文中，我们将PMP用于澳大利亚英语和印度英语的可解释讽刺检测，同时还引入了一个标准英语的基准数据集。我们在一个名为BESSTIE的现有澳大利亚英语和印度英语讽刺标注数据集上，手动添加了讽刺解释，并将二者的可解释讽刺检测性能与FLUTE（一个包含讽刺解释的标准英语数据集）进行了对比。我们的采用PMP的方法在两个开源大语言模型（GEMMA和LLAMA）上进行评估时，与四种其他提示策略相比，在所有任务和数据集上均实现了具有统计学意义的性能提升。我们还发现，智能体提示等替代技术能够通过支持外部知识检索来减少与上下文相关的失败情况。本工作的核心贡献是将PMP应用于为不同英语变体生成讽刺解释。
英文摘要：
Sarcasm is a challenge to sentiment analysis because of the incongruity between stated and implied sentiment. The challenge is exacerbated when the implication may be relevant to a specific country or geographical region. Pragmatic metacognitive prompting (PMP) is a cognition-inspired technique that has been used for pragmatic reasoning. In this paper, we harness PMP for explainable sarcasm detection for Australian and Indian English, alongside a benchmark dataset for standard English. We manually add sarcasm explanations to an existing sarcasm-labeled dataset for Australian and Indian English called BESSTIE, and compare the performance for explainable sarcasm detection for them with FLUTE, a standard English dataset containing sarcasm explanations. Our approach utilising PMP when evaluated on two open-weight LLMs (GEMMA and LLAMA) achieves statistically significant performance improvement across all tasks and datasets when compared with four alternative prompting strategies. We also find that alternative techniques such as agentic prompting mitigate context-related failures by enabling external knowledge retrieval. The focused contribution of our work is utilising PMP in generating sarcasm explanations for varieties of English.
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。
