@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://preview.aclanthology.org/fix-sig-urls/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."
}