@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T AutoIntent: AutoML for Text Classification
%A Alekseev, Ilya
%A Solomatin, Roman
%A Rustamova, Darina
%A Kuznetsov, Denis
%Y Habernal, Ivan
%Y Schulam, Peter
%Y Tiedemann, Jörg
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-334-0
%F alekseev-etal-2025-autointent
%X 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.
%U https://aclanthology.org/2025.emnlp-demos.53/
%P 707-716
Markdown (Informal)
[AutoIntent: AutoML for Text Classification](https://aclanthology.org/2025.emnlp-demos.53/) (Alekseev et al., EMNLP 2025)
ACL
- Ilya Alekseev, Roman Solomatin, Darina Rustamova, and Denis Kuznetsov. 2025. AutoIntent: AutoML for Text Classification. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 707–716, Suzhou, China. Association for Computational Linguistics.