Neural Topic Modeling with Bidirectional Adversarial Training

ACL 2020 Rui WangXuemeng HuDeyu ZhouYulan HeYuxuan XiongChenchen YeHaiyang Xu

Recent years have witnessed a surge of interests of using neural topic models for automatic topic extraction from text, since they avoid the complicated mathematical derivations for model inference as in traditional topic models such as Latent Dirichlet Allocation (LDA). However, these models either typically assume improper prior (e.g. Gaussian or Logistic Normal) over latent topic space or could not infer topic distribution for a given document... (read more)

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