SS-MPC: A Sequence-Structured Multi-Party Conversation System

ACL ARR 2025 May Submission5881 Authors

Abstract: Recent Multi-Party Conversation (MPC) models typically rely on graph-based approaches to capture dialogue structures. However, these methods have limitations, such as information loss during the projection of utterances into structural embeddings and constraints in leveraging pre-trained language models directly. In this paper, we propose **SS-MPC**, a response generation model for MPC that eliminates the need for explicit graph structures. Unlike existing models that depend on graphs to analyze conversation structures, SS-MPC internally encodes the dialogue structure as a sequential input, enabling direct utilization of pre-trained language models. Experimental results show that **SS-MPC** achieves **15.60\% BLEU-1** and **12.44\% ROUGE-L** score, outperforming the current state-of-the-art MPC response generation model by **3.91\%p** in **BLEU-1** and **0.62\%p** in **ROUGE-L**. In addition, human evaluation confirms that SS-MPC generates more fluent and accurate responses compared to existing MPC models.

Paper Type: Long

Research Area: Dialogue and Interactive Systems

Research Area Keywords: conversational modeling

Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models

Languages Studied: English

Submission Number: 5881