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

• URL: http://arxiv.org/abs/2505.21033v1
• Date: Tue, 27 May 2025 11:07:53 GMT
• Title: Def-DTS: Deductive Reasoning for Open-domain Dialogue Topic Segmentation
• Authors: Seungmin Lee, Yongsang Yoo, Minhwa Jung, Min Song,
• Abstract summary: This paper introduces Def-DTS: Deductive Reasoning for Open-domain Dialogue Topic.<n>Our method employs a structured prompting approach for bidirectional context summarization, utterance intent classification, and deductive topic shift detection.<n> Experiments in various dialogue settings demonstrate that Def-DTS consistently outperforms traditional and state-of-the-art approaches.
• Score: 2.1594788541056467
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Improving Dialogue Discourse Parsing through Discourse-aware Utterance Clarification [14.879100851573998]
  We propose a discourse-aware Clarification Module (DCM) to enhance the performance of the dialogue discourse.<n>DCM employs two distinct reasoning processes: clarification type reasoning and discourse goal reasoning.<n>CPO enables to assess the contributions of the clarifications from DCM and provide feedback to optimize the DCM.
  arXiv  Detail & Related papers  (2025-06-18T02:47:14Z)
• DistinctAD: Distinctive Audio Description Generation in Contexts [62.58375366359421]
  We propose DistinctAD, a framework for generating Audio Descriptions that emphasize distinctiveness to produce better narratives.<n>To address the domain gap, we introduce a CLIP-AD adaptation strategy that does not require additional AD corpora.<n>In Stage-II, DistinctAD incorporates two key innovations: (i) a Contextual Expectation-Maximization Attention (EMA) module that reduces redundancy by extracting common bases from consecutive video clips, and (ii) an explicit distinctive word prediction loss that filters out repeated words in the context.
  arXiv  Detail & Related papers  (2024-11-27T09:54:59Z)
• An Unsupervised Dialogue Topic Segmentation Model Based on Utterance Rewriting [3.5399864027190366]
  This study proposes a novel unsupervised dialog topic segmentation method that combines the Utterance Rewriting (UR) technique with an unsupervised learning algorithm. The proposed Discourse Rewriting Topic Model (UR-DTS) significantly improves the accuracy of topic segmentation.
  arXiv  Detail & Related papers  (2024-09-12T00:27:31Z)
• Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models [52.24756457516834]
  We propose a novel "Self-Explanation" prompting strategy to enhance the comprehension abilities of Large Language Models (LLMs) This task-agnostic approach requires the model to analyze each dialogue utterance before task execution, thereby improving performance across various dialogue-centric tasks. Experimental results from six benchmark datasets confirm that our method consistently outperforms other zero-shot prompts and matches or exceeds the efficacy of few-shot prompts.
  arXiv  Detail & Related papers  (2023-09-22T15:41:34Z)
• Re-Reading Improves Reasoning in Large Language Models [87.46256176508376]
  We introduce a simple, yet general and effective prompting method, Re2, to enhance the reasoning capabilities of off-the-shelf Large Language Models (LLMs) Unlike most thought-eliciting prompting methods, such as Chain-of-Thought (CoT), Re2 shifts the focus to the input by processing questions twice, thereby enhancing the understanding process. We evaluate Re2 on extensive reasoning benchmarks across 14 datasets, spanning 112 experiments, to validate its effectiveness and generality.
  arXiv  Detail & Related papers  (2023-09-12T14:36:23Z)
• Unsupervised Dialogue Topic Segmentation with Topic-aware Utterance Representation [51.22712675266523]
  Dialogue Topic (DTS) plays an essential role in a variety of dialogue modeling tasks. We propose a novel unsupervised DTS framework, which learns topic-aware utterance representations from unlabeled dialogue data.
  arXiv  Detail & Related papers  (2023-05-04T11:35:23Z)
• Multi-Stage Coarse-to-Fine Contrastive Learning for Conversation Intent Induction [34.25242109800481]
  This paper presents our solution to Track 2 of Intent Induction from Conversations for Task-Oriented Dialogue at the Eleventh Dialogue System Technology Challenge (DSTC11) The essence of intention clustering lies in distinguishing the representation of different dialogue utterances. In the released DSTC11 evaluation results, our proposed system ranked first on both of the two subtasks of this Track.
  arXiv  Detail & Related papers  (2023-03-09T04:51:27Z)
• Collaborative Reasoning on Multi-Modal Semantic Graphs for Video-Grounded Dialogue Generation [53.87485260058957]
  We study video-grounded dialogue generation, where a response is generated based on the dialogue context and the associated video. The primary challenges of this task lie in (1) the difficulty of integrating video data into pre-trained language models (PLMs) We propose a multi-agent reinforcement learning method to collaboratively perform reasoning on different modalities.
  arXiv  Detail & Related papers  (2022-10-22T14:45:29Z)
• GRASP: Guiding model with RelAtional Semantics using Prompt [3.1275060062551208]
  We propose a Guiding model with RelAtional Semantics using Prompt (GRASP) We adopt a prompt-based fine-tuning approach and capture relational semantic clues of a given dialogue with an argument-aware prompt marker strategy. In the experiments, GRASP state-of-the-art performance in terms of both F1 and F1c scores on a DialogRE dataset.
  arXiv  Detail & Related papers  (2022-08-26T08:19:28Z)
• End-to-End Active Speaker Detection [58.7097258722291]
  We propose an end-to-end training network where feature learning and contextual predictions are jointly learned. We also introduce intertemporal graph neural network (iGNN) blocks, which split the message passing according to the main sources of context in the ASD problem. Experiments show that the aggregated features from the iGNN blocks are more suitable for ASD, resulting in state-of-the art performance.
  arXiv  Detail & Related papers  (2022-03-27T08:55:28Z)
• Topic-Aware Multi-turn Dialogue Modeling [91.52820664879432]
  This paper presents a novel solution for multi-turn dialogue modeling, which segments and extracts topic-aware utterances in an unsupervised way. Our topic-aware modeling is implemented by a newly proposed unsupervised topic-aware segmentation algorithm and Topic-Aware Dual-attention Matching (TADAM) Network.
  arXiv  Detail & Related papers  (2020-09-26T08:43:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.