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Lifelong topic modeling with knowledge-enhanced adversarial network

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Abstract

Lifelong topic modeling has attracted much attention in natural language processing (NLP), since it can accumulate knowledge learned from past for the future task. However, the existing lifelong topic models often require complex derivation or only utilize part of the context information. In this study, we propose a knowledge-enhanced adversarial neural topic model (KATM) and extend it to LKATM for lifelong topic modeling. KATM employs a knowledge extractor to encourage the generator to learn interpretable document representations and retrieve knowledge from the generated documents. LKATM incorporates knowledge from the previous trained KATM into the current model to learn from prior models without catastrophic forgetting. Experiments on four benchmark text streams validate the effectiveness of our KATM and LKATM in topic discovery and document classification.

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  1. https://cs.nyu.edu/~roweis/data.html

  2. http://www.nltk.org/data.html

  3. http://aksw.org/Projects/Palmetto.html

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Acknowledgements

The research described in this paper was supported by the National Natural Science Foundation of China (61972426), Guangdong Basic and Applied Basic Research Foundation (2020A1515010536), the Hong Kong Research Grants Council (project no. PolyU 11204919), and an internal research grant from the Hong Kong Polytechnic University (project 1.9B0V).

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  1. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China

    Xuewen Zhang & Yanghui Rao

  2. Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Qing Li

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  1. Xuewen Zhang
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Correspondence to Yanghui Rao.

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Zhang, X., Rao, Y. & Li, Q. Lifelong topic modeling with knowledge-enhanced adversarial network. World Wide Web 25, 219–238 (2022). https://doi.org/10.1007/s11280-021-00984-2

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