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Cited by 32Speech and dialogue systemsAI in Service InteractionsTopic Modeling

Dialogue Intent Classification with Long Short-Term Memory Networks

Meng, Lian, Huang, Minlie·2018·Lecture Notes in Computer Science
DOI 10.1007/978-3-319-73618-1_4
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Crossref · 10.1007/978-3-319-73618-1_4

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1 of 1 references verified0 citation errors · 0 not checkable

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  • Memory-augmented networks have not fully addressed the need for a reliable and adaptable long-term memory function in LLMs.

    “Despite showing potential, these methods have not fully addressed the need for a reliable and adaptable long-term memory function in LLMs.”

    From MemoryBank: Enhancing Large Language Models with Long-Term Memory (2023) · cites this paper as Meng & Huang, 2018

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Authors
Meng, L., & Huang, M.
Affiliations
State Key Laboratory of Intelligent Technology and Systems (CN)
ORCID
Huang, Minlie
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2018
Pages
42-50
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-319-73618-1_4
OpenAlex
W2782096898
Cited by
32
References
1
Citations by year
2025: 3 · 2024: 4 · 2023: 5 · 2022: 2 · 2021: 6 · 2020: 6 · 2019: 5 · 2018: 1
FWCI
1.84
Type
conference paper
Language
en
Open access
closed
Licence
www.springer.com/tdm (unspecified)
Locations
Lecture notes in computer science (publishedVersion)
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
long short-term memory networks, hierarchical LSTM, memory modules, context modeling, dialogue systems, conversational datasets, e-commerce customer service

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