Topic Memory Networks for Short Text Classification
This addresses classification challenges for short texts, offering a novel end-to-end approach that is incremental over prior methods.
The paper tackles the problem of poor classification performance on short texts due to data sparsity by proposing topic memory networks, which outperform state-of-the-art models on four benchmark datasets and generate coherent topics.
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.