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Found 4 papers, 1 papers with code
S2vNTM: Semi-supervised vMF Neural Topic Modeling
Across a variety of datasets, S2vNTM outperforms existing semi-supervised topic modeling methods in classification accuracy with limited keywords provided.
KDSTM: Neural Semi-supervised Topic Modeling with Knowledge Distillation
In text classification tasks, fine tuning pretrained language models like BERT and GPT-3 yields competitive accuracy; however, both methods require pretraining on large text datasets.
vONTSS: vMF based semi-supervised neural topic modeling with optimal transport
Recently, Neural Topic Models (NTM), inspired by variational autoencoders, have attracted a lot of research interest; however, these methods have limited applications in the real world due to the challenge of incorporating human knowledge.
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Topic Models
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FFPDG: Fast, Fair and Private Data Generation
Generative modeling has been used frequently in synthetic data generation.
