DeepDialogue：一个多轮情感丰富的口语对话数据集
DeepDialogue: A Multi-Turn Emotionally-Rich Spoken Dialogue Dataset
AI总结：
本文提出DeepDialogue，一个包含40,150个多轮对话、覆盖41个领域和20种情感的大规模多模态数据集，通过9种语言模型配对生成并经过质量过滤，并合成情感一致的语音，揭示了模型规模、领域具体性和跨模型交互对对话连贯性的影响。
摘要：
近年来，对话式人工智能的进展已在单轮响应中展现出令人印象深刻的能力，然而即便是最复杂的语言模型，多轮对话仍然具有挑战性。当前对话数据集在情感范围、领域多样性、对话轮次深度方面存在局限，且主要以文本为主，这阻碍了开发更类人的跨模态对话系统的进展。为解决这些局限，我们提出了DeepDialogue，一个大规模多模态数据集，包含40,150个高质量多轮对话，覆盖41个领域，并融入20种不同情感且具有连贯的情感进展。我们的方法将9种不同的语言模型（参数规模从4B到72B）配对，生成65,600个初始对话，随后通过人工标注和基于LLM的质量过滤相结合的方式进行评估。由此产生的数据集揭示了基本洞见：较小的模型在超过6轮对话后难以维持连贯性；具体领域（如“汽车”、“旅行”）比抽象领域（如“哲学”）产生更有意义的对话；跨模型交互比同模型对话产生更连贯的对话。DeepDialogue的一个关键贡献是其语音组件，我们为所有40,150个对话合成了情感一致的语音，创建了首个大规模开源多模态对话数据集，在多轮对话中忠实保留了情感上下文。
英文摘要：
Recent advances in conversational AI have demonstrated impressive capabilities in single-turn responses, yet multi-turn dialogues remain challenging for even the most sophisticated language models. Current dialogue datasets are limited in their emotional range, domain diversity, turn depth, and are predominantly text-only, hindering progress in developing more human-like conversational systems across modalities. To address these limitations, we present DeepDialogue, a large-scale multimodal dataset containing 40,150 high-quality multi-turn dialogues spanning 41 domains and incorporating 20 distinct emotions with coherent emotional progressions. Our approach pairs 9 different language models (4B-72B parameters) to generate 65,600 initial conversations, which we then evaluate through a combination of human annotation and LLM-based quality filtering. The resulting dataset reveals fundamental insights: smaller models fail to maintain coherence beyond 6 dialogue turns; concrete domains (e.g., "cars," "travel") yield more meaningful conversations than abstract ones (e.g., "philosophy"); and cross-model interactions produce more coherent dialogues than same-model conversations. A key contribution of DeepDialogue is its speech component, where we synthesize emotion-consistent voices for all 40,150 dialogues, creating the first large-scale open-source multimodal dialogue dataset that faithfully preserves emotional context across multi-turn conversations.
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。
