@inproceedings{alekseev-etal-2025-autointent,
    title = "{A}uto{I}ntent: {A}uto{ML} for Text Classification",
    author = "Alekseev, Ilya  and
      Solomatin, Roman  and
      Rustamova, Darina  and
      Kuznetsov, Denis",
    editor = {Habernal, Ivan  and
      Schulam, Peter  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-demos.53/",
    pages = "707--716",
    ISBN = "979-8-89176-334-0",
    abstract = "AutoIntent is an automated machine learning tool for text classification tasks. Unlike existing solutions, AutoIntent offers end-to-end automation with embedding model selection, classifier optimization, and decision threshold tuning, all within a modular, sklearn-like interface. The framework is designed to support multi-label classification and out-of-scope detection. AutoIntent demonstrates superior performance compared to existing AutoML tools on standard intent classification datasets and enables users to balance effectiveness and resource consumption."
}