不用图结构，用序列编码对话，提升多人对话生成效果。
SS-MPC: A Sequence-Structured Multi-Party Conversation System
- 将对话结构转为序列输入，直接使用预训练语言模型。
- BLEU-1达15.60%，ROUGE-L达12.44%，优于当前最佳模型。
- 生成对话更流畅准确，适合需要自然多人交互的场景。
现有多人对话模型多依赖图结构捕捉对话关系，但存在话语投影时信息丢失、难以直接利用预训练语言模型等问题。本文提出SS-MPC，一种无需显式图结构的对话响应生成模型。该模型将对话结构内化为序列输入，实现对预训练语言模型的直接应用。实验显示，SS-MPC在对话生成任务中取得15.60% BLEU-1和12.44% ROUGE-L的成绩，相比当前最优模型分别提升3.91%和0.62%。人工评估也证实其生成内容更具流畅性与准确性。
原文摘要 · Abstract (English)
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 \textbf{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 \textbf{SS-MPC} achieves \textbf{15.60\% BLEU-1} and \textbf{12.44\% ROUGE-L} score, outperforming the current state-of-the-art MPC response generation model by \textbf{3.91\%p} in \textbf{BLEU-1} and \textbf{0.62\%p} in \textbf{ROUGE-L}. Additionally, human evaluation confirms that SS-MPC generates more fluent and accurate responses compared to existing MPC models.
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