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Supervised Neural Clustering via Latent Structured Output Learning: Application to Question Intents
Iryna Haponchyk, Alessandro Moschitti
Previous pre-neural work on structured prediction has produced very effective supervised clustering algorithms using linear classifiers, e.g., structured SVM or perceptron. However, these cannot exploit the representation learning ability of neural networks, which would make supervised clustering even more powerful, i.e., general clustering patterns can be learned automatically. In this paper, we design neural networks based on latent structured prediction loss and Transformer models to approach supervised clustering. We tested our methods on the task of automatically recreating categories of intents from publicly available question intent corpora. The results show that our approach delivers 95.65{\%} of F1, outperforming the state of the art by 17.24{\%}.
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