通过场景建模与意图关键词衔接，提升对话系统主动引导能力。
Enhancing Target-Guided Proactive Dialogue Systems via Conversational Scenario Modeling and Intent-Keyword Bridging
- 用用户画像与领域知识构建动态对话场景，影响系统回应
- 预测下一回合的意图关键词，实现更灵活的目标引导
- 显著提升对话的主动性、流畅性与信息量，接近真实交互
目标导向的主动对话系统旨在主动引导对话向预设目标（如特定关键词或话题）推进。在对话过程中，动态建模对话场景与意图关键词对生成系统回复具有重要意义；然而现有研究普遍忽视此点，导致与真实对话动态脱节。本文联合建模用户画像与领域知识作为对话场景，引入场景偏置以动态影响系统生成，并采用意图-关键词衔接机制预测下一回合的意图关键词，提供更高层次且更灵活的引导。大量自动与人工评估表明，该方法显著提升了目标导向主动对话系统的主动性、流畅性与信息量，有效缩小了与真实交互间的差距。
原文摘要 · Abstract (English)
A target-guided proactive dialogue system aims to steer conversations proactively toward pre-defined targets, such as designated keywords or specific topics. During guided conversations, dynamically modeling conversational scenarios and intent keywords to guide system utterance generation is beneficial; however, existing work largely overlooks this aspect, resulting in a mismatch with the dynamics of real-world conversations. In this paper, we jointly model user profiles and domain knowledge as conversational scenarios to introduce a scenario bias that dynamically influences system utterances, and employ intent-keyword bridging to predict intent keywords for upcoming dialogue turns, providing higher level and more flexible guidance. Extensive automatic and human evaluations demonstrate the effectiveness of conversational scenario modeling and intent keyword bridging, yielding substantial improvements in proactivity, fluency, and informativeness for target-guided proactive dialogue systems, thereby narrowing the gap with real world interactions.
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