Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus
Table 5: IMDB results, n/a stands for not applicable for the model (*NVDM, LDA and Scholar results are borrowed from card2018neural)
['Metrics', 'Accuracy', 'Perplexity', 'Coherence']
The classification results are consistent with the COVID-19 experiment. Baseline BERT has better accuracy than SCHOLAR, and CANTM further improves over BERT by about 0.5%. The topic modelling performance of CANTM is almost the same as for NVDM, while LDA and SCHOLAR have the best performance in perplexity and coherence, respectively.