# "Stupid robot, I want to speak to a human!" User Frustration Detection in Task-Oriented Dialog Systems

arXiv: https://arxiv.org/abs/2411.17437 · HTML: https://mlsanity.com/paper/2411.17437/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Simulating User Satisfaction for the Evaluation of Task-oriented Dialogue Systems](https://mlsanity.com/paper/2105.03748/) (2021)
- [Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations](https://mlsanity.com/paper/2010.02495/) (2020)
- [Multi-domain Conversation Quality Evaluation via User Satisfaction Estimation](https://mlsanity.com/paper/1911.08567/) (2019)
- [Domain-Independent turn-level Dialogue Quality Evaluation via User Satisfaction Estimation](https://mlsanity.com/paper/1908.07064/) (2019)
- [Different Types of Voice User Interface Failures May Cause Different Degrees of Frustration](https://mlsanity.com/paper/2002.03582/) (2020)
- [Revealing User Familiarity Bias in Task-Oriented Dialogue via Interactive Evaluation](https://mlsanity.com/paper/2305.13857/) (2023)

## Built on by (most cited first, 1 of 1)

- [ReIn: Conversational Error Recovery with Reasoning Inception](https://mlsanity.com/paper/2602.17022/) (2026)

## Abstract (arXiv)

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.