  To overcome the data sparsity issue in short text topic modeling, existing
methods commonly rely on data augmentation or the data characteristic of short
texts to introduce more word co-occurrence information. However, most of them
do not make full use of the augmented data or the data characteristic: they
insufficiently learn the relations among samples in data, leading to dissimilar
topic distributions of semantically similar text pairs. To better address data
sparsity, in this paper we propose a novel short text topic modeling framework,
Topic-Semantic Contrastive Topic Model (TSCTM). To sufficiently model the
relations among samples, we employ a new contrastive learning method with
efficient positive and negative sampling strategies based on topic semantics.
This contrastive learning method refines the representations, enriches the
learning signals, and thus mitigates the sparsity issue. Extensive experimental
results show that our TSCTM outperforms state-of-the-art baselines regardless
of the data augmentation availability, producing high-quality topics and topic
distributions.
