  Many classification models work poorly on short texts due to data sparsity.
To address this issue, we propose topic memory networks for short text
classification with a novel topic memory mechanism to encode latent topic
representations indicative of class labels. Different from most prior work that
focuses on extending features with external knowledge or pre-trained topics,
our model jointly explores topic inference and text classification with memory
networks in an end-to-end manner. Experimental results on four benchmark
datasets show that our model outperforms state-of-the-art models on short text
classification, meanwhile generates coherent topics.
