Enhancing Dialogue Analysis in Multiparty Meetings Through Argument and Relation Classification Models

Accurately analyzing dialogue in multi-party meetings is crucial for enhancing communication and decision-making. The informal and dynamic nature of these discussions introduces challenges for computational analysis, including non-standard language, interruptions, and rapid topic changes. To address these challenges, we developed two methods: Argument Classification and Relation Classification. Argument Classification employs machine learning models like Gradient Boosting to identify and categorize key points made by participants. This method improves conversation traceability and insight extraction, achieving an F1 score of 0.79, a 28% improvement over previous methods. Relation Classification uses advanced neural network architectures, such as BERT, to decode interactions and map relationships within discussions. This method captures conversational nuances, achieving an overall accuracy of 0.86 and an F1 score of 0.83. Our models effectively transform raw meeting transcripts into structured data, enhancing the understanding and analysis of dialogues. This approach not only clarifies discussions but also lays a foundation for better decision-making processes. The results demonstrate the potential for deploying these models in various domains requiring nuanced dialogue analysis.