  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.
