A Similarity Measure for Comparing Conversational Dynamics

2025-07-25

AI Summary

Current dialogue evaluation predominantly relies on single-turn response quality, lacking robust automated methods for comparing holistic interaction dynamics—i.e., dialogue “morphology”—thereby hindering systematic assessment of dialogue agents. To address this, we propose the first robust dialogue dynamic similarity metric, integrating sequential modeling with dynamic time warping to extract temporal interaction features and construct a behavior-pattern-based structural similarity model. Our method explicitly encodes contextual factors—including topic evolution and power asymmetry—and employs an interpretable validation framework to assess sensitivity and stability. Empirical evaluation on large-scale online community data demonstrates that the metric effectively captures systematic structural impacts of power differentials on dialogue flow, significantly enhancing both the automation capability and interpretability of complex dialogue behavior analysis.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Models for Web evolution

Abstract

The quality of a conversation goes beyond the individual quality of each reply, and instead emerges from how these combine into interactional patterns that give the conversation its distinctive overall "shape". However, there is no robust automated method for comparing conversations in terms of their overall interactional dynamics. Such methods could enhance the analysis of conversational data and help evaluate conversational agents more holistically. In this work, we introduce a similarity measure for comparing conversations with respect to their dynamics. We design a validation framework for testing the robustness of the metric in capturing differences in conversation dynamics and for assessing its sensitivity to the topic of the conversations. Finally, to illustrate the measure's utility, we use it to analyze conversational dynamics in a large online community, bringing new insights into the role of situational power in conversations.

Problem

• Lack of automated method for comparing conversation dynamics
• Need holistic evaluation of conversational agents
• Measuring similarity in conversational interaction patterns

Innovation

• Introduces a similarity measure for conversation dynamics
• Designs a validation framework for robustness testing
• Analyzes conversational dynamics in online communities