The DOI matches its Crossref record, with a small difference in title or year; its reference list is not available.
Keep Me Updated! Memory Management in Long-term Conversations
Open accessAbstract
Sanghwan Bae, Donghyun Kwak, Soyoung Kang, Min Young Lee, Sungdong Kim, Yuin Jeong, Hyeri Kim, Sang-Woo Lee, Woomyoung Park, Nako Sung. Findings of the Association for Computational Linguistics: EMNLP 2022. 2022.
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Existing long-term memory methods typically follow two directions: continuously recording, summarizing, and updating dialogue histories as memory, or transforming conversations into structured graph-based memory using OpenIE.
“These approaches typically follow two directions: (i) continuously recording, summarizing, and updating dialogue histories as memory [2, 23, 39, 44], or (ii) transforming conversations into structured graphbased memory using techniques such as OpenIE [19, 40, 57, 58].”
From HingeMem: Boundary Guided Long-Term Memory with Query Adaptive Retrieval for Scalable Dialogues (2026) · cites this paper as [2]
In conversational AI, well-designed memory structures are essential for maintaining personalized interactions across multiple sessions.
“In conversational AI, well-designed memory structures are essential for maintaining personalized interactions across multiple sessions (Martins et al., 2022; Bae et al., 2022; Gutiérrez et al., 2024).”
From Pre-Storage Reasoning for Episodic Memory: Shifting Inference Burden to Memory for Personalized Dialogue (2025) · cites this paper as Bae et al., 2022
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References 46 · Cited by 29
Metadata
- Authors
- Bae, S., Kwak, D., Kang, S., Lee, M. Y., Kim, S., Jeong, Y., Kim, H., Lee, S.-W., Park, W., & Sung, N.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- NAVER Cloud (South Korea) (KR)
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Published
- 2022
- Pages
- 3769-3787
- OpenAlex
- W4385573343
- Cited by
- 29
- References
- 46
- Citations by year
- 2026: 11 · 2025: 9 · 2024: 2 · 2023: 7
- FWCI
- 2.49
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- memory management, dialogue systems, chatbots
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