Cited by 216Speech and dialogue systemsTopic ModelingAI in Service Interactions

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Beyond Goldfish Memory: Long-Term Open-Domain Conversation

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Xu, Jing, Szlam, Arthur, Weston, Jason·2022·Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

DOI 10.18653/v1/2022.acl-long.356

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Abstract

Despite recent improvements in open-domain dialogue models, state-of-the-art models are trained and evaluated on short conversations with little context.In contrast, the long-term conversation setting has hardly been studied.In this work we collect and release a humanhuman dataset consisting of multiple chat sessions whereby the speaking partners learn about each other's interests and discuss the things they have learnt from past sessions.We show how existing models trained on existing datasets perform poorly in this long-term conversation setting in both automatic and human evaluations, and we study long-context models that can perform much better.In particular, we find retrieval-augmented methods and methods with an ability to summarize and recall previous conversations outperform the standard encoder-decoder architectures currently considered state-of-the-art.* We use this term colloquially, see Agranoff et al. (1965) for evidence of goldfish long-term memory.

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Authors: Xu, J., Szlam, A., & Weston, J.
Affiliations: Meta (United States) (US)
ORCID: Xu, Jing
Venue: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Publisher: Association for Computational Linguistics
Published: 2022
Pages: 5180-5197
ISSN: 0736-587X (ISSN-L)
DOI: 10.18653/v1/2022.acl-long.356
OpenAlex: W3186804217
Cited by: 188
References: 42
Citations by year: 2026: 33 · 2025: 30 · 2024: 32 · 2023: 51 · 2022: 36
FWCI: 19.38
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)
arXiv (Cornell University) (submittedVersion, public-domain)
arXiv (Cornell 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, long-context models, retrieval-augmented generation, dialogue summarization, chatbot evaluation

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Beyond Goldfish Memory: Long-Term Open-Domain Conversation

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