DASH: Dialogue-Aware Similarity and Handshake Recognition for Topic Segmentation in Public-Channel Conversations
Dialogue Topic Segmentation (DTS) is crucial for understanding task-oriented public-channel communications, such as maritime VHF dialogues, which feature informal speech and implicit transitions. To address the limitations of traditional methods, we propose DASH-DTS, a novel LLM-based framework. Its core contributions are: (1) topic shift detection via dialogue handshake recognition; (2) contextual enhancement through similarity-guided example selection; and (3) the generation of selective positive and negative samples to improve model discrimination and robustness. Additionally, we release VHF-Dial, the first public dataset of real-world maritime VHF communications, to advance research in this domain. DASH-DTS provides interpretable reasoning and confidence scores for each segment. Experimental results demonstrate that our framework achieves several sota segmentation trusted accuracy on both VHF-Dial and standard benchmarks, establishing a strong foundation for stable monitoring and decision support in operational dialogues.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
MHDash: An Online Platform for Benchmarking Mental Health-Aware AI Assistants
Large language models (LLMs) are increasingly applied in mental health support systems, where reliable recognition of high-risk states such as suicidal ideation and self-harm is safety-critical. However, existing evaluat…
Dialogue GenerationUnsupervised Dialogue Topic Segmentation with Topic-aware Utterance Representation
Dialogue Topic Segmentation (DTS) plays an essential role in a variety of dialogue modeling tasks. Previous DTS methods either focus on semantic similarity or dialogue coherence to assess topic similarity for unsupervise…
SegmentationSemantic SimilaritySemantic Textual SimilarityDASH: Dynamic Audio-Driven Semantic Chunking for Efficient Omnimodal Token Compression
Omnimodal large language models (OmniLLMs) jointly process audio and visual streams, but the resulting long multimodal token sequences make inference prohibitively expensive. Existing compression methods typically rely o…
Utterance Position-Aware Dialogue Act Recognition
This study proposes an utterance position-aware approach for a neural network-based dialogue act recognition (DAR) model, which incorporates positional encoding for utterance’s absolute or relative position. The proposed…
PositionNeural Recognition of Dashed Curves With Gestalt Law of Continuity
Dashed curve is a frequently used curve form and is widely used in various drawing and illustration applications. While humans can intuitively recognize dashed curves from disjoint curve segments based on the law of …