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History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System
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With the evolution of pre-trained language models, current open-domain dialogue systems have achieved great progress in conducting one-session conversations.In contrast, Multi-Session Conversation (MSC), which consists of multiple sessions over a long term with the same user, is under-investigated.In this paper, we propose History-Aware Hierarchical Transformer (HAHT) for multi-session opendomain dialogue.HAHT maintains a long-term memory of history conversations and utilizes history information to understand current conversation context and generate well-informed and context-relevant responses.Specifically, HAHT first encodes history conversation sessions hierarchically into a history memory.Then, HAHT leverages historical information to facilitate the understanding of the current conversation context by encoding the history memory together with the current context with attention-based mechanisms.Finally, to explicitly utilize historical information, HAHT uses a history-aware response generator that switches between a generic vocabulary and a historyaware vocabulary.Experimental results on a large-scale MSC dataset suggest that the proposed HAHT model consistently outperforms baseline models.Human evaluation results support that HAHT generates more human-like, context-relevant and history-relevant responses than baseline models.
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The long-term conversation setting is a hard problem because it requires understanding and memorizing key dialogue history information.
“Among them, the long-term conversation setting is pretty a hard problem, because it needs the capability of understanding and memorizing key dialogue history information (Wu et al., 2022;Zhang et al., 2022).”
From Recursively summarizing enables long-term dialogue memory in large language models (2025) · cites this paper as Zhang et al., 2022
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Recursively summarizing enables long-term dialogue memory in large language models
Wang, Q., Fu, Y., Cao, Y., Wang, S., Tian, Z., & Ding, L. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130193 · Cited by 55
Metadata
- Authors
- Zhang, T., Liu, Y., Li, B., Zeng, Z., Wang, P., You, Y., Miao, C., & Cui, L.
- Affiliations
- Nanyang Technological University (SG)
- Alibaba Group (China) (CN)
- Shandong University (CN)
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Published
- 2022
- Pages
- 3395-3407
- OpenAlex
- W4385567026
- Cited by
- 14
- References
- 10
- Citations by year
- 2026: 7 · 2025: 5 · 2024: 2
- FWCI
- 0.97
- 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)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Research Foundation (NRF-NRFF13-2021-0006)
- Nanyang Technological University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Keywords
- open-domain dialogue systems, dialogue generation
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