Multimodal intent recognition poses significant challenges, requiring the incorporation of non-verbal modalities from real-world contexts to enhance the comprehension of human intentions. Existing benchmark datasets are limited in scale and suffer from difficulties in handling out-of-scope samples that arise in multi-turn conversational interactions. We introduce MIntRec2.0, a large-scale benchmark dataset for multimodal intent recognition in multi-party conversations. It contains 1,245 dialogues with 15,040 samples, each annotated within a new intent taxonomy of 30 fine-grained classes. Besides 9,304 in-scope samples, it also includes 5,736 out-of-scope samples appearing in multi-turn contexts, which naturally occur in real-world scenarios. Furthermore, we provide comprehensive information on the speakers in each utterance, enriching its utility for multi-party conversational research. We establish a general framework supporting the organization of single-turn and multi-turn dialogue data, modality feature extraction, multimodal fusion, as well as in-scope classification and out-of-scope detection. Evaluation benchmarks are built using classic multimodal fusion methods, ChatGPT, and human evaluators. While existing methods incorporating nonverbal information yield improvements, effectively leveraging context information and detecting out-of-scope samples remains a substantial challenge. Notably, large language models exhibit a significant performance gap compared to humans, highlighting the limitations of machine learning methods in the cognitive intent understanding task. We believe that MIntRec2.0 will serve as a valuable resource, providing a pioneering foundation for research in human-machine conversational interactions, and significantly facilitating related applications. The full dataset and codes are available at https://github.com/thuiar/MIntRec2.0.

翻译：多模态意图识别面临重大挑战，需结合真实场景中的非语言模态以增强对人类意图的理解。现有基准数据集规模有限，且难以处理多轮对话交互中出现的域外样本。我们提出MIntRec2.0——一个面向多方对话中多模态意图识别的大规模基准数据集。该数据集包含1,245段对话及15,040个样本，每个样本均按照包含30个细粒度类别的新意图分类体系进行标注。除9,304个域内样本外，还包含5,736个多轮对话中自然出现的域外样本，这些样本真实反映现实场景特征。同时，我们提供每条话语的说话者完整信息，增强了其在多方对话研究中的实用性。我们构建了通用框架，支持单轮与多轮对话数据组织、模态特征提取、多模态融合、域内分类及域外检测。基于经典多模态融合方法、ChatGPT及人工评估者建立了评估基准。现有方法虽能通过整合非语言信息取得改进，但有效利用上下文信息与检测域外样本仍是重大挑战。值得注意的是，大型语言模型在认知意图理解任务中的表现与人类存在显著差距，揭示了机器学习方法在该领域的局限性。我们相信MIntRec2.0将作为重要资源，为人机对话交互研究提供开创性基础，并显著推动相关应用发展。完整数据集与代码见https://github.com/thuiar/MIntRec2.0。