  Distributional text clustering delivers semantically informative
representations and captures the relevance between each word and semantic
clustering centroids. We extend the neural text clustering approach to text
classification tasks by inducing cluster centers via a latent variable model
and interacting with distributional word embeddings, to enrich the
representation of tokens and measure the relatedness between tokens and each
learnable cluster centroid. The proposed method jointly learns word clustering
centroids and clustering-token alignments, achieving the state of the art
results on multiple benchmark datasets and proving that the proposed
cluster-token alignment mechanism is indeed favorable to text classification.
Notably, our qualitative analysis has conspicuously illustrated that text
representations learned by the proposed model are in accord well with our
intuition.
