Attentive Representation Learning with Adversarial Training for Short Text Clustering
This work addresses the challenge of sparse representations in short text clustering, which is important for applications like corpus summarization and information retrieval, but it appears incremental as it builds on existing clustering and adversarial training methods.
The paper tackles the problem of short text clustering by proposing a novel attentive representation learning model with adversarial training, achieving superior performance over strong competitors on four real-world datasets.
Short text clustering has far-reaching effects on semantic analysis, showing its importance for multiple applications such as corpus summarization and information retrieval. However, it inevitably encounters the severe sparsity of short text representations, making the previous clustering approaches still far from satisfactory. In this paper, we present a novel attentive representation learning model for shot text clustering, wherein cluster-level attention is proposed to capture the correlations between text representations and cluster representations. Relying on this, the representation learning and clustering for short texts are seamlessly integrated into a unified model. To further ensure robust model training for short texts, we apply adversarial training to the unsupervised clustering setting, by injecting perturbations into the cluster representations. The model parameters and perturbations are optimized alternately through a minimax game. Extensive experiments on four real-world short text datasets demonstrate the superiority of the proposed model over several strong competitors, verifying that robust adversarial training yields substantial performance gains.