@inproceedings{wang-etal-2025-demo,
title = "{DEMO}: Reframing Dialogue Interaction with Fine-grained Element Modeling",
author = "Wang, Minzheng and
Zhang, Xinghua and
Chen, Kun and
Xu, Nan and
Yu, Haiyang and
Huang, Fei and
Mao, Wenji and
Li, Yongbin",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.594/",
doi = "10.18653/v1/2025.findings-acl.594",
pages = "11373--11401",
ISBN = "979-8-89176-256-5",
abstract = "Large language models (LLMs) enabled dialogue systems have become one of the central modes in human-machine interaction, which bring about vast amounts of conversation logs and increasing demand for dialogue generation. The dialogue{'}s life-cycle spans from $\textit{Prelude}$ through $\textit{Interlocution}$ to $\textit{Epilogue}$, encompassing rich dialogue elements. Despite large volumes of dialogue-related studies, there is a lack of systematic investigation into the dialogue stages to frame benchmark construction that covers comprehensive dialogue elements. This hinders the precise modeling, generation and assessment of LLMs-based dialogue systems. To bridge this gap, in this paper, we introduce a new research task{---}$\textbf{D}$ialogue $\textbf{E}$lement $\textbf{MO}$deling, including $\textit{Element Awareness}$ and $\textit{Dialogue Agent Interaction}$, and propose a novel benchmark, $\textbf{DEMO}$, designed for a comprehensive dialogue modeling and assessment. On this basis, we further build the DEMO agent with the adept ability to model dialogue elements via imitation learning. Extensive experiments on DEMO indicate that current representative LLMs still have considerable potential for enhancement, and our DEMO agent performs well in both dialogue element modeling and out-of-domain tasks."
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<abstract>Large language models (LLMs) enabled dialogue systems have become one of the central modes in human-machine interaction, which bring about vast amounts of conversation logs and increasing demand for dialogue generation. The dialogue’s life-cycle spans from Prelude through Interlocution to Epilogue, encompassing rich dialogue elements. Despite large volumes of dialogue-related studies, there is a lack of systematic investigation into the dialogue stages to frame benchmark construction that covers comprehensive dialogue elements. This hinders the precise modeling, generation and assessment of LLMs-based dialogue systems. To bridge this gap, in this paper, we introduce a new research task—Dialogue Element MOdeling, including Element Awareness and Dialogue Agent Interaction, and propose a novel benchmark, DEMO, designed for a comprehensive dialogue modeling and assessment. On this basis, we further build the DEMO agent with the adept ability to model dialogue elements via imitation learning. Extensive experiments on DEMO indicate that current representative LLMs still have considerable potential for enhancement, and our DEMO agent performs well in both dialogue element modeling and out-of-domain tasks.</abstract>
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%0 Conference Proceedings
%T DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling
%A Wang, Minzheng
%A Zhang, Xinghua
%A Chen, Kun
%A Xu, Nan
%A Yu, Haiyang
%A Huang, Fei
%A Mao, Wenji
%A Li, Yongbin
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wang-etal-2025-demo
%X Large language models (LLMs) enabled dialogue systems have become one of the central modes in human-machine interaction, which bring about vast amounts of conversation logs and increasing demand for dialogue generation. The dialogue’s life-cycle spans from Prelude through Interlocution to Epilogue, encompassing rich dialogue elements. Despite large volumes of dialogue-related studies, there is a lack of systematic investigation into the dialogue stages to frame benchmark construction that covers comprehensive dialogue elements. This hinders the precise modeling, generation and assessment of LLMs-based dialogue systems. To bridge this gap, in this paper, we introduce a new research task—Dialogue Element MOdeling, including Element Awareness and Dialogue Agent Interaction, and propose a novel benchmark, DEMO, designed for a comprehensive dialogue modeling and assessment. On this basis, we further build the DEMO agent with the adept ability to model dialogue elements via imitation learning. Extensive experiments on DEMO indicate that current representative LLMs still have considerable potential for enhancement, and our DEMO agent performs well in both dialogue element modeling and out-of-domain tasks.
%R 10.18653/v1/2025.findings-acl.594
%U https://aclanthology.org/2025.findings-acl.594/
%U https://doi.org/10.18653/v1/2025.findings-acl.594
%P 11373-11401
Markdown (Informal)
[DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling](https://aclanthology.org/2025.findings-acl.594/) (Wang et al., Findings 2025)
ACL
- Minzheng Wang, Xinghua Zhang, Kun Chen, Nan Xu, Haiyang Yu, Fei Huang, Wenji Mao, and Yongbin Li. 2025. DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling. In Findings of the Association for Computational Linguistics: ACL 2025, pages 11373–11401, Vienna, Austria. Association for Computational Linguistics.