A Persona-Based Neural Conversation Model
Open accessAbstract
Jiwei Li, Michel Galley, Chris Brockett, Georgios Spithourakis, Jianfeng Gao, Bill Dolan. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.
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Li et al. (2016) developed persona-based neural conversation models with a notion of speakers' style, identity and consistency.
“Li et al. (2016) developed persona-based neural conversation models with a notion of speakers' style, identity and consistency [18].”
From Memory Fabric for Conversational AI Agents: Enabling Shared and Persistent Memory Across Users (2025) · cites this paper as [18]
Once the speaker embedding is injected into the decoder, responses are more stable and consistent over conversations.
“Once the speaker embedding is injected into the decoder, the responses are more stable and consistent over conversations.”
From Memory Fabric for Conversational AI Agents: Enabling Shared and Persistent Memory Across Users (2025) · cites this paper as [18]
Each speaker is assigned a vector embedding representing their persona, learned together with other aspects of the model during training.
“Each speaker is assigned a vector embedding that represents their persona.These are learned together with other aspects of the model during training.”
From Memory Fabric for Conversational AI Agents: Enabling Shared and Persistent Memory Across Users (2025) · cites this paper as [18]
Provenance tracking based on recency and reliability with algorithms for reconciling contradictory information can help maintain consistency and prevent propagation of errors.
“Provenance tracking based on recency and reliability with algorithms for reconciling contradictory information can help maintain consistency and prevent propagation of errors [18,30].”
From Memory Fabric for Conversational AI Agents: Enabling Shared and Persistent Memory Across Users (2025) · cites this paper as [18]
The Introduction and §6.4 discuss consistency of a conversational agent’s responses, not provenance tracking. Searches for recency, source reliability, reconciliation of contradictory information, and error propagation found no passage addressing the claimed approach. (LLM, conf=0.76)
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- Authors
- Li, J., Galley, M., Brockett, C., Spithourakis, G., Gao, J., & Dolan, B.
- Affiliations
- Stanford University (US)
- Microsoft (United States) (US)
- University College London (GB)
- Venue
- Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2016
- Pages
- 994-1003
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2964352131
- Cited by
- 898
- References
- 2
- Citations by year
- 2026: 28 · 2025: 27 · 2024: 37 · 2023: 85 · 2022: 83 · 2021: 138 · 2020: 161 · 2019: 144 · 2018: 132 · 2017: 53 · 2016: 9 · 2015: 1
- FWCI
- 60.71
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- chatbots, dialogue modeling
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