Stickers, while widely recognized for enhancing empathetic communication in online interactions, remain underexplored in current empathetic dialogue research, notably due to the challenge of a lack of comprehensive datasets. In this paper, we introduce the Agent for STICKERCONV (Agent4SC), which uses collaborative agent interactions to realistically simulate human behavior with sticker usage, thereby enhancing multimodal empathetic communication. Building on this foundation, we develop a multimodal empathetic dialogue dataset, STICKERCONV, comprising 12.9K dialogue sessions, 5.8K unique stickers, and 2K diverse conversational scenarios. This dataset serves as a benchmark for multimodal empathetic generation. To advance further, we propose PErceive and Generate Stickers (PEGS), a multimodal empathetic response generation framework, complemented by a comprehensive set of empathy evaluation metrics based on LLM. Our experiments demonstrate PEGS's effectiveness in generating contextually relevant and emotionally resonant multimodal empathetic responses, contributing to the advancement of more nuanced and engaging empathetic dialogue systems.

翻译：贴纸虽然被广泛认为能在在线互动中增强共情交流，但在当前的共情对话研究中仍未被充分探索，这主要源于缺乏全面数据集的挑战。本文引入了STICKERCONV代理（Agent4SC），通过协作代理交互真实模拟人类使用贴纸的行为，从而增强多模态共情交流。在此基础上，我们构建了多模态共情对话数据集STICKERCONV，包含12.9K轮对话、5.8K张独特贴纸和2K种多样化对话场景。该数据集可作为多模态共情生成的基准。为进一步推进研究，我们提出了感知与生成贴纸（PEGS）框架，这是一个多模态共情回复生成框架，并辅以一套基于大语言模型的全面共情评估指标。实验证明，PEGS在生成上下文相关且情感共鸣的多模态共情回复方面具有有效性，为发展更细致、更具吸引力的共情对话系统做出了贡献。