Personalizing Dialogue Agents: I have a dog, do you have pets too?
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Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, Jason Weston. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018.
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Existing NLP datasets do not cover the wide breadth of possible conversational failure cases due to their emphasis on correct data for training.
“We found that existing NLP datasets do not cover the wide breadth of possible conversational failure cases due to their emphasis on correct data for training.”
From A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures (2023) · cites this paper as [68]
The Introduction and Related Work say existing dialogue datasets have limitations that contribute to problems such as inconsistent personality and weak long-term coherence. They do not say that NLP datasets fail to cover the wide breadth of possible conversational failure cases, or attribute such a gap to an emphasis on correct training data. Page numbers are unavailable in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPersona consistency has been the center of attention and is one of the most obvious cases of self-contradiction regarding the character of the dialogue system.
“Persona consistency has been the center of attention [144, 312] and it is one of the most obvious cases of self-contradiction regarding the character of the dialogue system.”
From Survey of Hallucination in Natural Language Generation (2023) · cites this paper as [312]
The PersonaChat datasets have accelerated research into persona consistency.
“The PersonaChat datasets [47, 312] have accelerated research into persona consistency [96, 128, 178, 284, 296, 307, 318].”
From Survey of Hallucination in Natural Language Generation (2023) · cites this paper as [312]
Existing datasets like personalized chats sourced through crowdsourcing or psychological dialogues lack the property of containing valuable personal memories.
“especially considering that existing datasets like personalized chats sourced through crowdsourcing (Zhang et al., 2018) or psychological dialogues (Zhong et al., 2024) lack this property.”
From Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs (2024) · cites this paper as Zhang et al., 2018
Standard NLP models are often evaluated on large datasets of coherent text-based questions and answers or paired written dialogue.
“Standard NLP models are often evaluated on large datasets of coherent text-based questions and answers [48, 49] or paired written dialogue [68].”
From A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures (2023) · cites this paper as [68]
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Background
- Authors
- Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., & Weston, J.
- Affiliations
- Mila - Quebec Artificial Intelligence Institute (CA)
- Meta (United States) (US)
- Corresponding author
- Zhang, Saizheng
- ORCID
- Dinan, Emily
- Venue
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2018
- Pages
- 2204-2213
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2963825865
- Cited by
- 1,177
- References
- 1
- Citations by year
- 2026: 73 · 2025: 97 · 2024: 92 · 2023: 231 · 2022: 191 · 2021: 216 · 2020: 164 · 2019: 97 · 2018: 16
- FWCI
- 50.31
- 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 › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- dialogue agents, personalization, chatbots, dialogue modeling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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