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Recent work incorporates pre-trained word embeddings such as BERT embeddingsinto Neural Topic Models (NTMs), generating highly coherent topics. However,with high-quality contextualized document representations, do we really needsophisticated neural models to obtain coherent and interpretable topics? Inthis paper, we conduct thorough experiments showing that directly clusteringhigh-quality sentence embeddings with an appropriate word selecting method cangenerate more coherent and diverse topics than NTMs, achieving also higherefficiency and simplicity.

Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics