@inproceedings{fernandez-etal-2024-incremental,
title = "Incremental Learning for Knowledge-Grounded Dialogue Systems in Industrial Scenarios",
author = "Fernandez, Izaskun and
Aceta, Cristina and
Fernandez, Cristina and
Torres, Maria Ines and
Etxalar, Aitor and
Mendez, Ariane and
Agirre, Maia and
Torralbo, Manuel and
Del Pozo, Arantza and
Agirre, Joseba and
Artetxe, Egoitz and
Altuna, Iker",
editor = "Kawahara, Tatsuya and
Demberg, Vera and
Ultes, Stefan and
Inoue, Koji and
Mehri, Shikib and
Howcroft, David and
Komatani, Kazunori",
booktitle = "Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2024",
address = "Kyoto, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.sigdial-1.8",
pages = "92--102",
abstract = "In today{'}s industrial landscape, seamless collaboration between humans and machines is essential and requires a shared knowledge of the operational domain. In this framework, the technical knowledge for operator assistance has traditionally been derived from static sources such as technical documents. However, experienced operators hold invaluable know-how that can significantly contribute to support other operators. This work focuses on enhancing the operator assistance tasks in the manufacturing industry by leveraging spoken natural language interaction. More specifically, a Human-in-the-Loop (HIL) incremental learning approach is proposed to integrate this expertise into a domain knowledge graph (KG) dynamically, along with the use of in-context learning for Large Language Models (LLMs) to benefit other capabilities of the system. Preliminary results of the experimentation carried out in an industrial scenario, where the graph size was increased in a 25{\%}, demonstrate that the incremental enhancing of the KG benefits the dialogue system{'}s performance.",
}
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%0 Conference Proceedings
%T Incremental Learning for Knowledge-Grounded Dialogue Systems in Industrial Scenarios
%A Fernandez, Izaskun
%A Aceta, Cristina
%A Fernandez, Cristina
%A Torres, Maria Ines
%A Etxalar, Aitor
%A Mendez, Ariane
%A Agirre, Maia
%A Torralbo, Manuel
%A Del Pozo, Arantza
%A Agirre, Joseba
%A Artetxe, Egoitz
%A Altuna, Iker
%Y Kawahara, Tatsuya
%Y Demberg, Vera
%Y Ultes, Stefan
%Y Inoue, Koji
%Y Mehri, Shikib
%Y Howcroft, David
%Y Komatani, Kazunori
%S Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2024
%8 September
%I Association for Computational Linguistics
%C Kyoto, Japan
%F fernandez-etal-2024-incremental
%X In today’s industrial landscape, seamless collaboration between humans and machines is essential and requires a shared knowledge of the operational domain. In this framework, the technical knowledge for operator assistance has traditionally been derived from static sources such as technical documents. However, experienced operators hold invaluable know-how that can significantly contribute to support other operators. This work focuses on enhancing the operator assistance tasks in the manufacturing industry by leveraging spoken natural language interaction. More specifically, a Human-in-the-Loop (HIL) incremental learning approach is proposed to integrate this expertise into a domain knowledge graph (KG) dynamically, along with the use of in-context learning for Large Language Models (LLMs) to benefit other capabilities of the system. Preliminary results of the experimentation carried out in an industrial scenario, where the graph size was increased in a 25%, demonstrate that the incremental enhancing of the KG benefits the dialogue system’s performance.
%U https://aclanthology.org/2024.sigdial-1.8
%P 92-102
Markdown (Informal)
[Incremental Learning for Knowledge-Grounded Dialogue Systems in Industrial Scenarios](https://aclanthology.org/2024.sigdial-1.8) (Fernandez et al., SIGDIAL 2024)
ACL
- Izaskun Fernandez, Cristina Aceta, Cristina Fernandez, Maria Ines Torres, Aitor Etxalar, Ariane Mendez, Maia Agirre, Manuel Torralbo, Arantza Del Pozo, Joseba Agirre, Egoitz Artetxe, and Iker Altuna. 2024. Incremental Learning for Knowledge-Grounded Dialogue Systems in Industrial Scenarios. In Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 92–102, Kyoto, Japan. Association for Computational Linguistics.