@inproceedings{hernandez-caralt-etal-2025-stupid,
title = "{\textquotedblleft}Stupid robot, {I} want to speak to a human!{\textquotedblright} User Frustration Detection in Task-Oriented Dialog Systems",
author = "Hernandez Caralt, Mireia and
Sekulic, Ivan and
Carevic, Filip and
Khau, Nghia and
Popa, Diana Nicoleta and
Guedes, Bruna and
Guimaraes, Victor and
Yang, Zeyu and
Manso, Andre and
Reddy, Meghana and
Rosso, Paolo and
Mathis, Roland",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven and
Darwish, Kareem and
Agarwal, Apoorv",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: Industry Track",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-industry.23/",
pages = "276--285",
abstract = "Detecting user frustration in modern-day task-oriented dialog (TOD) systems is imperative for maintaining overall user satisfaction, engagement, and retention. However, most recent research is focused on sentiment and emotion detection in academic settings, thus failing to fully encapsulate implications of real-world user data. To mitigate this gap, in this work, we focus on user frustration in a deployed TOD system, assessing the feasibility of out-of-the-box solutions for user frustration detection. Specifically, we compare the performance of our deployed keyword-based approach, open-source approaches to sentiment analysis, dialog breakdown detection methods, and emerging in-context learning LLM-based detection. Our analysis highlights the limitations of open-source methods for real-world frustration detection, while demonstrating the superior performance of the LLM-based approach, achieving a 16{\%} relative improvement in F1 score on an internal benchmark. Finally, we analyze advantages and limitations of our methods and provide an insight into user frustration detection task for industry practitioners."
}
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<abstract>Detecting user frustration in modern-day task-oriented dialog (TOD) systems is imperative for maintaining overall user satisfaction, engagement, and retention. However, most recent research is focused on sentiment and emotion detection in academic settings, thus failing to fully encapsulate implications of real-world user data. To mitigate this gap, in this work, we focus on user frustration in a deployed TOD system, assessing the feasibility of out-of-the-box solutions for user frustration detection. Specifically, we compare the performance of our deployed keyword-based approach, open-source approaches to sentiment analysis, dialog breakdown detection methods, and emerging in-context learning LLM-based detection. Our analysis highlights the limitations of open-source methods for real-world frustration detection, while demonstrating the superior performance of the LLM-based approach, achieving a 16% relative improvement in F1 score on an internal benchmark. Finally, we analyze advantages and limitations of our methods and provide an insight into user frustration detection task for industry practitioners.</abstract>
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%0 Conference Proceedings
%T “Stupid robot, I want to speak to a human!” User Frustration Detection in Task-Oriented Dialog Systems
%A Hernandez Caralt, Mireia
%A Sekulic, Ivan
%A Carevic, Filip
%A Khau, Nghia
%A Popa, Diana Nicoleta
%A Guedes, Bruna
%A Guimaraes, Victor
%A Yang, Zeyu
%A Manso, Andre
%A Reddy, Meghana
%A Rosso, Paolo
%A Mathis, Roland
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%Y Darwish, Kareem
%Y Agarwal, Apoorv
%S Proceedings of the 31st International Conference on Computational Linguistics: Industry Track
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F hernandez-caralt-etal-2025-stupid
%X Detecting user frustration in modern-day task-oriented dialog (TOD) systems is imperative for maintaining overall user satisfaction, engagement, and retention. However, most recent research is focused on sentiment and emotion detection in academic settings, thus failing to fully encapsulate implications of real-world user data. To mitigate this gap, in this work, we focus on user frustration in a deployed TOD system, assessing the feasibility of out-of-the-box solutions for user frustration detection. Specifically, we compare the performance of our deployed keyword-based approach, open-source approaches to sentiment analysis, dialog breakdown detection methods, and emerging in-context learning LLM-based detection. Our analysis highlights the limitations of open-source methods for real-world frustration detection, while demonstrating the superior performance of the LLM-based approach, achieving a 16% relative improvement in F1 score on an internal benchmark. Finally, we analyze advantages and limitations of our methods and provide an insight into user frustration detection task for industry practitioners.
%U https://aclanthology.org/2025.coling-industry.23/
%P 276-285
Markdown (Informal)
[“Stupid robot, I want to speak to a human!” User Frustration Detection in Task-Oriented Dialog Systems](https://aclanthology.org/2025.coling-industry.23/) (Hernandez Caralt et al., COLING 2025)
ACL
- Mireia Hernandez Caralt, Ivan Sekulic, Filip Carevic, Nghia Khau, Diana Nicoleta Popa, Bruna Guedes, Victor Guimaraes, Zeyu Yang, Andre Manso, Meghana Reddy, Paolo Rosso, and Roland Mathis. 2025. “Stupid robot, I want to speak to a human!” User Frustration Detection in Task-Oriented Dialog Systems. In Proceedings of the 31st International Conference on Computational Linguistics: Industry Track, pages 276–285, Abu Dhabi, UAE. Association for Computational Linguistics.