{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/supervised-neural-clustering-via-latent","title":"Supervised Neural Clustering via Latent Structured Output Learning: Application to Question Intents","arxiv_id":null,"date":"2021-06-01","proceeding":"NAACL 2021 4","authors":["Iryna Haponchyk","Alessandro Moschitti"],"abstract":"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{\\%}.","url_abs":"https://aclanthology.org/2021.naacl-main.263","url_pdf":"https://aclanthology.org/2021.naacl-main.263.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"supervised-neural-clustering-via-latent","repo_url":"https://github.com/ikernels/intent-qa","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"structured-prediction","task_name":"Structured Prediction"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"svm","method_name":"SVM"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}