Neural Text Classification by Jointly Learning to Cluster and Align
This work provides an incremental improvement for text classification tasks by enriching token representations through cluster-token alignment.
This paper extends neural text clustering to text classification by using a latent variable model to induce cluster centers and interact with word embeddings. The method jointly learns word clustering centroids and clustering-token alignments, achieving state-of-the-art results on multiple benchmark datasets.
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.