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        <title>Dynamic Structured Neural Topic Model with Self-Attention Mechanism</title>
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    <name type="personal">
        <namePart type="given">Nozomu</namePart>
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    <name type="personal">
        <namePart type="given">Masaru</namePart>
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    <name type="personal">
        <namePart type="given">Junichiro</namePart>
        <namePart type="family">Mori</namePart>
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    <name type="personal">
        <namePart type="given">Ichiro</namePart>
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    <abstract>This study presents a dynamic structured neural topic model, which can handle the time-series development of topics while capturing their dependencies. Our model captures the topic branching and merging processes by modeling topic dependencies based on a self-attention mechanism. Additionally, we introduce citation regularization, which induces attention weights to represent citation relations by modeling text and citations jointly. Our model outperforms a prior dynamic embedded topic model regarding perplexity and coherence, while maintaining sufficient diversity across topics. Furthermore, we confirm that our model can potentially predict emerging topics from academic literature.</abstract>
    <identifier type="citekey">miyamoto-etal-2023-dynamic</identifier>
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%0 Conference Proceedings
%T Dynamic Structured Neural Topic Model with Self-Attention Mechanism
%A Miyamoto, Nozomu
%A Isonuma, Masaru
%A Takase, Sho
%A Mori, Junichiro
%A Sakata, Ichiro
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F miyamoto-etal-2023-dynamic
%X This study presents a dynamic structured neural topic model, which can handle the time-series development of topics while capturing their dependencies. Our model captures the topic branching and merging processes by modeling topic dependencies based on a self-attention mechanism. Additionally, we introduce citation regularization, which induces attention weights to represent citation relations by modeling text and citations jointly. Our model outperforms a prior dynamic embedded topic model regarding perplexity and coherence, while maintaining sufficient diversity across topics. Furthermore, we confirm that our model can potentially predict emerging topics from academic literature.
%R 10.18653/v1/2023.findings-acl.366
%U https://aclanthology.org/2023.findings-acl.366
%U https://doi.org/10.18653/v1/2023.findings-acl.366
%P 5916-5930

Markdown (Informal)

[Dynamic Structured Neural Topic Model with Self-Attention Mechanism](https://aclanthology.org/2023.findings-acl.366) (Miyamoto et al., Findings 2023)

• Dynamic Structured Neural Topic Model with Self-Attention Mechanism (Miyamoto et al., Findings 2023)

ACL

• Nozomu Miyamoto, Masaru Isonuma, Sho Takase, Junichiro Mori, and Ichiro Sakata. 2023. Dynamic Structured Neural Topic Model with Self-Attention Mechanism. In Findings of the Association for Computational Linguistics: ACL 2023, pages 5916–5930, Toronto, Canada. Association for Computational Linguistics.