The advancement of large language models (LLMs) has propelled the development of dialogue systems. Unlike the popular ChatGPT-like assistant model, which only satisfies the user's preferences, task-oriented dialogue systems have also faced new requirements and challenges in the broader business field. They are expected to provide correct responses at each dialogue turn, at the same time, achieve the overall goal defined by the task. By understanding rhetorical structures and topic structures via topic segmentation and discourse parsing, a dialogue system may do a better planning to achieve both objectives. However, while both structures belong to discourse structure in linguistics, rhetorical structure and topic structure are mostly modeled separately or with one assisting the other in the prior work. The interaction between these two structures has not been considered for joint modeling and mutual learning. Furthermore, unsupervised learning techniques to achieve the above are not well explored. To fill this gap, we propose an unsupervised mutual learning framework of two structures leveraging the global and local connections between them. We extend the topic modeling between non-adjacent discourse units to ensure global structural relevance with rhetorical structures. We also incorporate rhetorical structures into the topic structure through a graph neural network model to ensure local coherence consistency. Finally, we utilize the similarity between the two fused structures for mutual learning. The experimental results demonstrate that our methods outperform all strong baselines on two dialogue rhetorical datasets (STAC and Molweni), as well as dialogue topic datasets (Doc2Dial and TIAGE).

翻译：大型语言模型（LLM）的发展推动了对话系统的进步。与当前流行的类ChatGPT助手模型仅满足用户偏好不同，面向任务的对话系统在更广泛的商业领域也面临着新的需求与挑战。这类系统不仅需要在每个对话轮次提供正确回应，同时还需完成任务定义的总体目标。通过话题分割与篇章解析来理解修辞结构与话题结构，对话系统可以更好地进行规划以实现这两个目标。然而，尽管这两种结构在语言学中均属于篇章结构范畴，现有研究大多将修辞结构与话题结构分开建模，或以其中一种辅助另一种建模，尚未考虑将两种结构的相互作用纳入联合建模与相互学习的框架中。此外，实现上述目标的无监督学习技术尚未得到充分探索。为填补这一空白，我们提出了一种利用两种结构间全局与局部关联的无监督相互学习框架。我们扩展了非相邻篇章单元间的话题建模，以确保其与修辞结构具有全局相关性；同时通过图神经网络模型将修辞结构融入话题结构，以保证局部连贯性一致。最后，我们利用两种融合结构之间的相似性进行相互学习。实验结果表明，我们的方法在两个对话修辞数据集（STAC与Molweni）以及对话话题数据集（Doc2Dial与TIAGE）上均优于所有强基线模型。