vONTSS: vMF based semi-supervised neural topic modeling with optimal transport
Weijie Xu, Xiaoyu Jiang, Srinivasan Sengamedu Hanumantha Rao, Francis Iannacci, Jinjin Zhao · 2023
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.This work presents a semi-supervised neural topic modeling method, vONTSS, which uses von Mises-Fisher (vMF) based variational autoencoders and optimal transport.When a few keywords per topic are provided, vONTSS in the semi-supervised setting generates potential topics and optimizes topic-keyword quality and topic classification.Experiments show that vONTSS outperforms existing semi-supervised topic modeling methods in classification accuracy and diversity.vONTSS also supports unsupervised topic modeling.Quantitative and qualitative experiments show that vONTSS in the unsupervised setting outperforms recent NTMs on multiple aspects: vONTSS discovers highly clustered and coherent topics on benchmark datasets.It is also much faster than the state-of-the-art weakly supervised text classification method while achieving similar classification performance.We further prove the equivalence of optimal transport loss and cross-entropy loss at the global minimum.