CobSeg: Coherence Boundary Modeling for Dialogue Topic Segmentation

CobSeg introduces a novel multi-branch architecture for dialogue topic segmentation that separates semantic continuity from lexical boundary transitions, achieving significant performance improvements across five benchmarks without requiring LLM calls during inference. The approach demonstrates particular strength in scenarios where local lexical cues are prominent, reducing error metrics substantially in both supervised and pseudo-label settings.

CobSeg addresses a fundamental challenge in natural language processing: accurately identifying topic boundaries in dialogue systems. Dialogue topic segmentation is essential for human-AI collaborative applications, where systems must recognize when conversations shift between subjects. The paper identifies a critical limitation in existing utterance models—they dilute local lexical signals that often indicate topic transitions. By proposing a multi-branch architecture that explicitly separates coherence-level semantic analysis from lexical boundary detection, CobSeg maintains sensitivity to both signal types.

The technical innovation reflects broader progress in NLP model architecture design. Rather than treating all utterance information uniformly, CobSeg implements directional boundary prediction with boundary informativeness weighting, emphasizing utterance positions that contain high-utility segmentation cues. This targeted approach demonstrates how architectural choices can preserve linguistic signals that monolithic models obscure.

The practical impact centers on inference efficiency and accessibility. CobSeg operates as a compact trainable segmenter that achieves enhanced performance without LLM calls during inference, addressing real deployment constraints where computational cost and latency matter significantly. Results across five benchmarks show meaningful improvements, particularly on VHF where Pk improved by 0.7 points under gold supervision and 14.8 points with induced boundaries.

Looking forward, the work suggests that specialized architectures for dialogue understanding can compete with or augment larger language models for specific tasks. The approach may influence how developers design dialogue systems that require efficient, interpretable topic segmentation without expensive model inference.

• →CobSeg's multi-branch architecture separately models semantic continuity and lexical boundaries, improving dialogue topic segmentation accuracy across five benchmarks
• →The approach eliminates the need for LLM calls during inference while maintaining performance gains, addressing practical deployment efficiency concerns
• →Boundary informativeness weighting enables the model to focus on utterance positions that carry the highest segmentation value
• →Performance improvements are most pronounced when local lexical cues are prominent, suggesting complementary strengths with semantic-focused approaches
• →Results demonstrate competitive performance with induced boundaries, indicating practical utility in scenarios lacking gold-standard annotations