我跑得像兔子一样快，你呢？一个多语言明喻对话数据集
I run as fast as a rabbit, can you? A Multilingual Simile Dialogue Dataset
AI总结：
本文提出最大规模的多语言明喻对话数据集 MSD（约 20K），涵盖英中双语，并设计明喻识别、解释、生成及对话检索、生成共 5 个任务，通过强基线模型实验验证了该数据集的挑战性。
摘要：
明喻是一种修辞手法，通过共享属性将两个不同的事物（分别称为本体和喻体）进行比较。本体和喻体通常通过“像”或“如”等比较词连接。在真实生活对话场景中，明喻现象独特而复杂，本体和喻体可以是动词短语或句子，可能由不同说话者提及，可能存在于不同句子中，也可能以相反顺序出现。然而，当前的明喻研究通常关注三元组（本体、属性、喻体）或单一句子中的明喻，其中本体和喻体通常是实体或名词短语，这无法反映真实场景中复杂的明喻现象。在本文中，我们提出了一个新颖且高质量的多语言明喻对话（MSD）数据集，以促进复杂明喻现象的研究。MSD 是最大的人工标注明喻数据集（约 20K），包含英文和中文数据。同时，MSD 数据也可用于对话任务，以测试对话系统在使用明喻时的能力。我们基于 MSD 设计了 3 个明喻任务（识别、解释和生成）和 2 个对话任务（检索和生成）。对于每个任务，我们提供了强预训练模型或最先进模型的实验结果。实验证明了 MSD 的挑战性，我们已在 GitHub 上发布了数据和代码。
英文摘要：
A simile is a figure of speech that compares two different things (called the tenor and the vehicle) via shared properties. The tenor and the vehicle are usually connected with comparator words such as "like" or "as". The simile phenomena are unique and complex in a real-life dialogue scene where the tenor and the vehicle can be verbal phrases or sentences, mentioned by different speakers, exist in different sentences, or occur in reversed order. However, the current simile research usually focuses on similes in a triplet tuple (tenor, property, vehicle) or a single sentence where the tenor and vehicle are usually entities or noun phrases, which could not reflect complex simile phenomena in real scenarios. In this paper, we propose a novel and high-quality multilingual simile dialogue (MSD) dataset to facilitate the study of complex simile phenomena. The MSD is the largest manually annotated simile data ($\sim$20K) and it contains both English and Chinese data. Meanwhile, the MSD data can also be used on dialogue tasks to test the ability of dialogue systems when using similes. We design 3 simile tasks (recognition, interpretation, and generation) and 2 dialogue tasks (retrieval and generation) with MSD. For each task, we provide experimental results from strong pre-trained or state-of-the-art models. The experiments demonstrate the challenge of MSD and we have released the data/code on GitHub.
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。
