对话中话语解析与主题分割的无监督互学习
Unsupervised Mutual Learning of Discourse Parsing and Topic Segmentation in Dialogue
AI总结：
针对对话中话语解析与主题分割的监督标注昂贵及无监督方法忽视二者交互的问题，提出统一表示与两个语言学假设，构建无监督互学习框架UMLF，联合建模修辞与主题结构，在多个数据集上超越强基线并有效提升大语言模型的话语结构建模。
摘要：
在对话系统中，话语在管理对话焦点和协调交互中起着至关重要的作用。它由两个关键结构组成：修辞结构和主题结构。前者捕捉对话的逻辑流程，而后者检测主题之间的转换。两者共同提高了对话系统跟踪对话动态并生成上下文相关的高质量响应的能力。这些结构通常分别通过话语解析和主题分割来识别。然而，现有的监督方法依赖于昂贵的人工标注，而无监督方法通常专注于单一任务，忽视了修辞结构与主题结构之间深层的语言交互。为了解决这些问题，我们首先引入了一种统一表示，该表示整合了修辞结构和主题结构，确保它们之间的语义一致性。在统一表示下，我们进一步基于话语理论提出了两个语言学依据的假设：（1）局部话语耦合，其中修辞线索动态增强主题感知的信息流；（2）全局拓扑约束，其中主题结构模式概率性地约束修辞关系分布。基于统一表示和两个假设，我们提出了一种无监督互学习框架（UMLF），该框架联合建模修辞结构和主题结构，使它们能够在无需额外标注的情况下相互增强。我们在两个修辞数据集和三个主题分割数据集上评估了我们的方法。实验结果表明，我们的方法超越了所有基于预训练语言模型的强基线。此外，当应用于大语言模型时，我们的框架取得了显著改进，证明了其在改进话语结构建模方面的有效性。
英文摘要：
In dialogue systems, discourse plays a crucial role in managing conversational focus and coordinating interactions. It consists of two key structures: rhetorical structure and topic structure. The former captures the logical flow of conversations, while the latter detects transitions between topics. Together, they improve the ability of a dialogue system to track conversation dynamics and generate contextually relevant high-quality responses. These structures are typically identified through discourse parsing and topic segmentation, respectively. However, existing supervised methods rely on costly manual annotations, while unsupervised methods often focus on a single task, overlooking the deep linguistic interplay between rhetorical and topic structures. To address these issues, we first introduce a unified representation that integrates rhetorical and topic structures, ensuring semantic consistency between them. Under the unified representation, we further propose two linguistically grounded hypotheses based on discourse theories: (1) Local Discourse Coupling, where rhetorical cues dynamically enhance topic-aware information flow, and (2) Global Topology Constraint, where topic structure patterns probabilistically constrain rhetorical relation distributions. Building on the unified representation and two hypotheses, we propose an unsupervised mutual learning framework (UMLF) that jointly models rhetorical and topic structures, allowing them to mutually reinforce each other without requiring additional annotations. We evaluate our approach on two rhetorical datasets and three topic segmentation datasets. Experimental results demonstrate that our method surpasses all strong baselines built on pre-trained language models. Furthermore, when applied to LLMs, our framework achieves notable improvements, demonstrating its effectiveness in improving discourse structure modeling.
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。
