@inproceedings{chen-etal-2025-compress,
title = "Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations",
author = "Chen, Nuo and
Li, Hongguang and
Chang, Jianhui and
Huang, Juhua and
Wang, Baoyuan and
Li, Jia",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.51/",
pages = "755--773",
abstract = "Existing retrieval-based methods have made significant strides in maintaining long-term conversations. However, these approaches face challenges in memory database management and accurate memory retrieval, hindering their efficacy in dynamic, real-world interactions. This study introduces a novel framework, COmpressive Memory-Enhanced Dialogue sYstems (COMEDY), which eschews traditional retrieval modules and memory databases. Instead, COMEDY adopts a {\textquotedblleft}One-for-All{\textquotedblright} approach, utilizing a single language model to manage memory generation, compression, and response generation. Central to this framework is the concept of compressive memory, which integrates session-specific summaries, user-bot dynamics, and past events into a concise memory format. To support COMEDY, we collect the biggest Chinese long-term conversation dataset, Dolphin, derived from real user-chatbot interactions. Comparative evaluations demonstrate COMEDY`s superiority over traditional retrieval-based methods in producing more nuanced and human-like conversational experiences."
}
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%0 Conference Proceedings
%T Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations
%A Chen, Nuo
%A Li, Hongguang
%A Chang, Jianhui
%A Huang, Juhua
%A Wang, Baoyuan
%A Li, Jia
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F chen-etal-2025-compress
%X Existing retrieval-based methods have made significant strides in maintaining long-term conversations. However, these approaches face challenges in memory database management and accurate memory retrieval, hindering their efficacy in dynamic, real-world interactions. This study introduces a novel framework, COmpressive Memory-Enhanced Dialogue sYstems (COMEDY), which eschews traditional retrieval modules and memory databases. Instead, COMEDY adopts a “One-for-All” approach, utilizing a single language model to manage memory generation, compression, and response generation. Central to this framework is the concept of compressive memory, which integrates session-specific summaries, user-bot dynamics, and past events into a concise memory format. To support COMEDY, we collect the biggest Chinese long-term conversation dataset, Dolphin, derived from real user-chatbot interactions. Comparative evaluations demonstrate COMEDY‘s superiority over traditional retrieval-based methods in producing more nuanced and human-like conversational experiences.
%U https://aclanthology.org/2025.coling-main.51/
%P 755-773
Markdown (Informal)
[Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations](https://aclanthology.org/2025.coling-main.51/) (Chen et al., COLING 2025)
ACL