从意图到对话：面向多轮分类的对比学习生成意图驱动对话方法
From Intents to Conversations: Generating Intent-Driven Dialogues with Contrastive Learning for Multi-Turn Classification
AI总结：
针对多轮意图分类缺乏大规模领域多语言数据的问题，提出融合HMM与LLM的Chain-of-Intent框架生成意图驱动对话，搭配MINT-CL多任务对比学习框架提升分类效果，还发布了电商领域多语言多轮对话语料库MINT-E。
摘要：
在对话式AI系统中，训练有效的多轮意图分类模型的一个关键挑战在于大规模、领域特定的多语言对话数据集的生成。本文提出了Chain-of-Intent（意图链）这一新型框架，该框架将隐马尔可夫模型（HMMs）与大语言模型（LLMs）相结合，通过自博弈生成意图驱动、具备上下文感知能力的对话。我们的方法首先从真实电商聊天日志中提取领域特定的意图转移模式，这些模式用于指导轮次级动态与意图序列的建模。随后利用LLMs对HMMs的发射概率进行参数化，从而生成与预测意图及对话上下文对齐的自然、连贯话语。我们还提出了MINT-CL，一种用于多轮意图分类的多任务对比学习框架，在提升性能的同时降低了对大规模标注数据集的依赖。实验结果表明，我们的方法在对话生成质量和分类准确率上均优于竞争基线，尤其在多语言场景下表现突出。为推动后续研究，我们发布了MINT-E，这是一个源自电商领域的综合性多语言、意图感知多轮对话语料库，复现代码与数据集可在https://github.com/junhua/chain-of-intent获取。
英文摘要：
In conversational AI systems, a critical challenge in training effective multi-turn intent classification models lies in the generation of large-scale, domain-specific, multilingual dialogue datasets. In this paper, we introduce Chain-of-Intent, a novel framework that integrates Hidden Markov Models (HMMs) with Large Language Models (LLMs) to generate intent-driven, context-aware dialogues through self-play. Our method first extracts domain-specific intent transition patterns from real-world e-commerce chat logs, which guide the modeling of turn-level dynamics and intent sequences. LLMs are then employed to parameterize the emission probabilities of HMMs, enabling the generation of natural, coherent utterances aligned with predicted intents and dialogue context. We also propose MINT-CL, a multi-task contrastive learning framework for multi-turn intent classification, which improves performance while reducing dependence on large-scale annotated datasets. Empirical results demonstrate that our approach outperforms competitive baselines in dialogue generation quality and classification accuracy, particularly in multilingual settings. To facilitate future research, we release MINT-E, a comprehensive, multilingual, intent-aware multi-turn dialogue corpus derived from the e-commerce domain\footnote{The reproduced source code and dataset are available at https://github.com/junhua/chain-of-intent.
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。
