This paper presents the results of the shared task on Chinese metaphor generation, hosted at the 13th CCF Conference on Natural Language Processing and Chinese Computing (NLPCC 2024). The goal of this shared task is to generate Chinese metaphors using machine learning techniques and effectively identifying basic components of metaphorical sentences. It is divided into two subtasks: 1) Metaphor Generation, which involves creating a metaphor from a provided tuple consisting of TENOR, GROUND, and VEHICLE. The goal here is to synthesize a metaphor that connects the subject (i.e. TENOR) with the object (i.e. VEHICLE), guided by the concept of the GROUND. 2) Metaphor Components Identification, which extracts the most fitting TENORs, GROUNDs, and VEHICLEs from a metaphorical sentence. This component requires the identification of the most fitting metaphor elements that correspond to the specified grounds. In addition to overall results, we report on the setup and insights from the metaphor generation shared task, which attracted a total of 4 participating teams across both subtasks.

翻译：本文介绍了在第十三届CCF自然语言处理与中文计算会议（NLPCC 2024）上举办的中文隐喻生成评测任务的结果。该评测任务的目标是运用机器学习技术生成中文隐喻，并有效识别隐喻句子的基本构成要素。任务分为两个子任务：1) 隐喻生成，即根据提供的由本体（TENOR）、喻底（GROUND）和喻体（VEHICLE）组成的元组生成一个隐喻句。此处的目标是在喻底概念的引导下，构建一个连接主体（即本体）与客体（即喻体）的隐喻。2) 隐喻成分识别，即从给定的隐喻句中抽取出最匹配的本体、喻底和喻体。该部分要求识别出与指定喻底相对应的最合适的隐喻要素。除了总体结果外，我们还报告了隐喻生成评测任务的设置与相关见解，该任务在两个子任务上共吸引了4支参赛队伍。