用大模型推理提升开放域对话分段准确率
Def-DTS: Deductive Reasoning for Open-domain Dialogue Topic Segmentation
- 通过多步逻辑推理分析对话上下文和意图
- 在多个场景下优于现有方法，尤其减少误判
- 支持中间结果分析，适合需要可解释性的研究
对话话题分割（DTS）旨在将对话划分为语义连贯的段落，在多种自然语言处理任务中至关重要，但长期面临数据不足、标注模糊及方法复杂度上升等问题。尽管大语言模型（LLM）与推理技术取得进展，却很少应用于DTS。本文提出Def-DTS：基于大模型的演绎推理开放域对话话题分割方法，采用结构化提示实现双向上下文摘要、话语意图分类与话题转移检测。在意图分类中，提出通用意图列表以支持跨领域意图识别。实验表明，Def-DTS在多种对话场景中持续优于传统与前沿方法，各子任务均贡献性能提升，尤其显著降低类型2错误。同时探索了自动标注潜力，凸显大模型推理在DTS中的价值。
原文摘要 · Abstract (English)
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.
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