Def- DTS : Deductive Reasoning for Open-domain Dialogue Topic Segmentation (2025)

Abstract: Dialogue Topic Segmentation (DTS) aims to divide dialogues into coherent segments. DTS plays a crucial role in various NLP downstream tasks, but suffers from chronic problems: data shortage, labeling ambiguity, and incremental complexity of recently proposed solutions. On the other hand, Despite advances in Large Language Models (LLMs) and reasoning strategies, these have rarely been applied to DTS. This paper introduces Def-DTS: Deductive Reasoning for Open-domain Dialogue Topic Segmentation, which utilizes LLM-based multi-step deductive reasoning to enhance DTS performance and enable case study using intermediate result. Our method employs a structured prompting approach for bidirectional context summarization, utterance intent classification, and deductive topic shift detection. In the intent classification process, we propose the generalizable intent list for domain-agnostic dialogue intent classification. Experiments in various dialogue settings demonstrate that Def-DTS consistently outperforms traditional and state-of-the-art approaches, with each subtask contributing to improved performance, particularly in reducing type 2 error. We also explore the potential for autolabeling, emphasizing the importance of LLM reasoning techniques in DTS.

## introduction dialogue topic segmentation (dts) is a task that aims to divide a dialogue into segments where each segment focuses on a coherent topic. figure 1 shows an example of topic shift within a single dialogue. dts is crucial for various natural language processing (nlp) tasks, including response prediction @xcite @xcite , response generation @xcite @xcite , dialogue state tracking @xcite , summarization (bokaei et al., 2016; @xcite @xcite , question answering @xcite , and machine reading comprehension @xcite . despite growing interest, dts suffers from several chronic challenges. first, the shortage of annotated data has led most recent dts studies to an unsupervised way, which generally yields suboptimal performance. second, ambiguity in segment labeling has hindered the development of effective approaches. lastly, recent studies: dialstart @xcite , ur-dts @xcite have proposed incremental approaches that require more parameter and complexity, enhancing previous studies: csm @xcite and dialstart @xcite , respectively. this progression indicates that dts is a challenging and often underestimated problem. while dts struggles with its complexities, nlp has witnessed significant advancements with the rise of large language models (llms) and reasoning methodologies. however, even considering the robust problem-solving skills of these llms and the challenges posed by dts, reasoning strategies are rarely applied in the dts area. this is because dts has been treated largely as a lightweight subtask in nlp. nonetheless, with the rise of ai-driven chat services, the demand for more advanced dts modules is growing. llms with reasoning capabilities are well suited to meet this need, making llm-based dts a viable solution. to establish clear criteria for topic shifts and simplify complex subtasks, we propose def-dts: a deductive reasoning approach for open-domain dialogue topic segmentation using llm-based multi-step reasoning. def-dts employs structured prompting to guide llms through bidirectional context summarization, utterance intent classification, and deductive topic shift detection at the utterance level, with an emphasis on domain-agnostic intent classification. to evaluate def-dts, we test it on three dialogue datasets spanning open-domain and task-oriented settings across three key metrics. our method consistently outperforms traditional and state-of-theart unsupervised, supervised, and prompt-based techniques by a significant margin. ablation studies confirm that each subtask improves overall performance, with intermediate intent classification particularly improving true-positive detection. finally, we explore llm-based auto-labeling for dts. our contributions are fourfold: ## conclusion previous approaches to dts have been constrained by several challenges, including data shortage, ambiguity of segment labeling, and increasingly complex model architectures. concurrently, the promising approach of reasoning with llms has yet to be explored in the context of dts. to address these issues, we propose def-dts, that leverages llms in conjunction with sophisticated reasoning strategies. def-dts incorporates bidirectional context extraction, a crucial component in previous research, along with the novel task of utterance intent classification. this approach demonstrates significant performance improvements in both the open-domain dialogue setting and the task-oriented dialogue setting. its efficacy across diverse datasets is enhanced through the provision of dataset-specific examples in the utterance intent classification task, enabling adaptable performance in varied dialogue contexts. through its primary findings and diverse analysis, we demonstrate the efficacy of llm-reasoning as a promising approach to dts. it not only highlights the potential of our method, but it also statistically delineates model tiage superdialseg dialseg711 the challenges to be addressed in future research. in subsequent investigations, we intend to explore the feasibility of automated labeling for dts and examine the potential integration of dts with other nlp downstream tasks through llm reasoning.