Sarcasm is a way of verbal irony where someone says the opposite of what they mean, often to ridicule a person, situation, or idea. It is often difficult to detect sarcasm in the dialogue since detecting sarcasm should reflect the context (i.e., dialogue history). In this paper, we introduce a new dataset for the Korean dialogue sarcasm detection task, KoCoSa (Korean Context-aware Sarcasm Detection Dataset), which consists of 12.8K daily Korean dialogues and the labels for this task on the last response. To build the dataset, we propose an efficient sarcasm detection dataset generation pipeline: 1) generating new sarcastic dialogues from source dialogues with large language models, 2) automatic and manual filtering of abnormal and toxic dialogues, and 3) human annotation for the sarcasm detection task. We also provide a simple but effective baseline for the Korean sarcasm detection task trained on our dataset. Experimental results on the dataset show that our baseline system outperforms strong baselines like large language models, such as GPT-3.5, in the Korean sarcasm detection task. We show that the sarcasm detection task relies deeply on the existence of sufficient context. We will release the dataset at https://anonymous.4open.science/r/KoCoSa-2372.

翻译：讽刺是一种言语反讽方式，即说话者表达与真实意图相反的内容，常用于嘲弄某人、某种情境或观点。由于讽刺检测需要反映上下文（即对话历史），因此在对话中识别讽刺往往具有挑战性。本文提出了一个用于韩语对话讽刺检测任务的新数据集KoCoSa（韩语上下文感知讽刺检测数据集），该数据集包含12.8K条韩语日常对话及其对最后一句回复的讽刺检测标签。为构建该数据集，我们提出了一套高效的讽刺检测数据集生成流程：1）利用大语言模型从源对话生成新的讽刺对话；2）通过自动过滤与人工过滤机制剔除异常和有害对话；3）针对讽刺检测任务进行人工标注。此外，我们基于该数据集训练并提供了一个简单有效的韩语讽刺检测基线系统。实验结果表明，在韩语讽刺检测任务中，我们的基线系统优于GPT-3.5等强基线大语言模型。我们证明讽刺检测任务高度依赖充足上下文的支撑。数据集将在https://anonymous.4open.science/r/KoCoSa-2372公开发布。