@inproceedings{kang-etal-2025-generation,
title = "Generation-Based and Emotion-Reflected Memory Update: Creating the {KEEM} Dataset for Better Long-Term Conversation",
author = "Kang, Jeonghyun and
Kim, Hongjin and
Kim, Harksoo",
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.623/",
pages = "9260--9277",
abstract = "In this work, we introduce the Keep Emotional and Essential Memory (KEEM) dataset, a novel generation-based dataset designed to enhance memory updates in long-term conversational systems. Unlike existing approaches that rely on simple accumulation or operation-based methods, which often result in information conflicts and difficulties in accurately tracking a user`s current state, KEEM dynamically generates integrative memories. This process not only preserves essential factual information but also incorporates emotional context and causal relationships, enabling a more nuanced understanding of user interactions. By seamlessly updating a system`s memory with both emotional and essential data, our approach promotes deeper empathy and enhances the system`s ability to respond meaningfully in open-domain conversations."
}
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<abstract>In this work, we introduce the Keep Emotional and Essential Memory (KEEM) dataset, a novel generation-based dataset designed to enhance memory updates in long-term conversational systems. Unlike existing approaches that rely on simple accumulation or operation-based methods, which often result in information conflicts and difficulties in accurately tracking a user‘s current state, KEEM dynamically generates integrative memories. This process not only preserves essential factual information but also incorporates emotional context and causal relationships, enabling a more nuanced understanding of user interactions. By seamlessly updating a system‘s memory with both emotional and essential data, our approach promotes deeper empathy and enhances the system‘s ability to respond meaningfully in open-domain conversations.</abstract>
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%0 Conference Proceedings
%T Generation-Based and Emotion-Reflected Memory Update: Creating the KEEM Dataset for Better Long-Term Conversation
%A Kang, Jeonghyun
%A Kim, Hongjin
%A Kim, Harksoo
%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 kang-etal-2025-generation
%X In this work, we introduce the Keep Emotional and Essential Memory (KEEM) dataset, a novel generation-based dataset designed to enhance memory updates in long-term conversational systems. Unlike existing approaches that rely on simple accumulation or operation-based methods, which often result in information conflicts and difficulties in accurately tracking a user‘s current state, KEEM dynamically generates integrative memories. This process not only preserves essential factual information but also incorporates emotional context and causal relationships, enabling a more nuanced understanding of user interactions. By seamlessly updating a system‘s memory with both emotional and essential data, our approach promotes deeper empathy and enhances the system‘s ability to respond meaningfully in open-domain conversations.
%U https://aclanthology.org/2025.coling-main.623/
%P 9260-9277
Markdown (Informal)
[Generation-Based and Emotion-Reflected Memory Update: Creating the KEEM Dataset for Better Long-Term Conversation](https://aclanthology.org/2025.coling-main.623/) (Kang et al., COLING 2025)
ACL