Accurate multi-turn intent classification is essential for advancing conversational AI systems. However, challenges such as the scarcity of comprehensive datasets and the complexity of contextual dependencies across dialogue turns hinder progress. This paper presents two novel approaches leveraging Large Language Models (LLMs) to enhance scalability and reduce latency in production dialogue systems. First, we introduce Symbol Tuning, which simplifies intent labels to reduce task complexity and improve performance in multi-turn dialogues. Second, we propose C-LARA (Consistency-aware, Linguistics Adaptive Retrieval Augmentation), a framework that employs LLMs for data augmentation and pseudo-labeling to generate synthetic multi-turn dialogues. These enriched datasets are used to fine-tune a small, efficient model suitable for deployment. Experiments conducted on multilingual dialogue datasets demonstrate significant improvements in classification accuracy and resource efficiency. Our methods enhance multi-turn intent classification accuracy by 5.09%, reduce annotation costs by 40%, and enable scalable deployment in low-resource multilingual industrial systems, highlighting their practicality and impact.

翻译：准确的多轮意图分类对于推进对话AI系统至关重要。然而，诸如全面数据集的稀缺性以及跨对话轮次的上下文依赖复杂性等挑战阻碍了进展。本文提出了两种利用大语言模型（LLMs）的新方法，以增强生产对话系统的可扩展性并降低延迟。首先，我们引入符号调优，该方法通过简化意图标签来降低任务复杂度并提升多轮对话中的性能。其次，我们提出C-LARA（一致性感知、语言自适应检索增强）框架，该框架利用LLMs进行数据增强和伪标注以生成合成的多轮对话。这些增强的数据集用于微调一个适合部署的小型高效模型。在多语言对话数据集上进行的实验表明，该方法在分类准确性和资源效率方面取得了显著提升。我们的方法将多轮意图分类准确率提高了5.09%，将标注成本降低了40%，并实现了在低资源多语言工业系统中的可扩展部署，凸显了其实用性和影响力。