  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). We provide our code at
https://github.com/Jeff-Sue/URT.
