Сравнение методов

	Классификация речевых актов	Заполнение слотов
Область	Интеллектуальный анализ текста	Интеллектуальный анализ текста
Семейство	Process / pipeline	Process / pipeline
Год появления	1997–2000	2018 (joint slot-gate model); BIO tagging foundations earlier
Автор метода	Stolcke et al.; Jurafsky et al.	Established via NER/IOB tagging literature; popularised for dialogue by Goo et al. (2018) and Chen et al. (2019)
Тип	NLP utterance-classification task	NLP token-classification / information-extraction task
Основополагающий источник	Stolcke, A. et al. (2000). Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech. Computational Linguistics, 26(3), 339-373.	Goo, C.W., Gao, G., Hsu, Y.K., Huo, C.L., Chen, T.C., Hsu, S.C., & Chen, Y.N. (2018). Slot-Gated Modeling for Joint Slot Filling and Intent Prediction. Proceedings of NAACL-HLT 2018.
Другие названия	dialogue act tagging, speech act classification, Diyalog Eylem Sınıflandırma (Dialogue Act Classification)	slot doldurma, Slot Doldurma (Slot Filling / NER-NLU), information slot extraction, dialogue slot filling
Связанные	4	5
Сводка	Dialogue act classification is a natural-language-processing task that automatically labels the communicative function of each utterance in a conversation — such as question, answer, greeting, or rejection. Consolidated by Jurafsky et al. (1997) and Stolcke et al. (2000), it is a foundational component for chatbots and discourse analysis.	Slot filling is a natural-language-understanding task that extracts predefined template fields — such as date, location, or product name — from a user utterance. It emerged as a core component of dialogue systems and form-based information extraction, and became widely studied after Goo et al. (2018) introduced the Slot-Gated Model for joint slot filling and intent prediction, followed by Chen et al. (2019) who extended the paradigm with BERT-based joint modelling.