Supervised Clustering Loss for Clustering-Friendly Sentence Embeddings: an Application to Intent Clustering
Giorgio Barnabò, Antonio Uva, Sandro Pollastrini, Chiara Rubagotti, Davide Bernardi · 2023
Modern virtual assistants are trained to classify customer requests into a taxonomy of predesigned intents.Requests that fall outside of this taxonomy, however, are often unhandled and need to be clustered to define new experiences.Recently, state-of-the-art results in intent clustering were achieved by training a neural network with a latent structured prediction loss.Unfortunately, though, this new approach suffers from a quadratic bottleneck as it requires to compute a joint embedding representation for all pairs of utterances to cluster.To overcome this limitation, we instead cast the problem into a representation learning task, and we adapt the latent structured prediction loss to fine-tune sentence encoders, thus making it possible to obtain clustering-friendly single-sentence embeddings.Our experiments show that the supervised clustering loss returns state-of-the-art results in terms of clustering accuracy and adjusted mutual information.