# Offline and Online Satisfaction Prediction in Open-Domain Conversational Systems

arXiv: https://arxiv.org/abs/2006.01921 · HTML: https://mlsanity.com/paper/2006.01921/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems](https://mlsanity.com/paper/2202.02912/) (2022)
- [HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations](https://mlsanity.com/paper/2106.00162/) (2021)
- [Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues](https://mlsanity.com/paper/2305.16798/) (2023)
- [Meta-evaluation of Conversational Search Evaluation Metrics](https://mlsanity.com/paper/2104.13453/) (2021)
- [Modeling User Satisfaction Dynamics in Dialogue via Hawkes Process](https://mlsanity.com/paper/2305.12594/) (2023)
- [What Went Wrong? Explaining Overall Dialogue Quality through Utterance-Level Impacts](https://mlsanity.com/paper/2111.00572/) (2021)
- [TWIZ-v2: The Wizard of Multimodal Conversational-Stimulus](https://mlsanity.com/paper/2310.02118/) (2023)
- [Rethinking the Evaluation of Dialogue Systems: Effects of User Feedback on Crowdworkers and LLMs](https://mlsanity.com/paper/2404.12994/) (2024)
- [Rating Prediction in Conversational Task Assistants with Behavioral and Conversational-Flow Features](https://mlsanity.com/paper/2309.11307/) (2023)
- [LLM-guided Plan and Retrieval: A Strategic Alignment for Interpretable User Satisfaction Estimation in Dialogue](https://mlsanity.com/paper/2503.04675/) (2025)
- [Minority-Aware Satisfaction Estimation in Dialogue Systems via Preference-Adaptive Reinforcement Learning](https://mlsanity.com/paper/2511.05407/) (2025)
- [Large-Scale User Behavior Analysis in Multimodal AI-Assisted Manual Task Execution](https://mlsanity.com/paper/2609.07594/) (2026)

## Abstract (arXiv)

Predicting user satisfaction in conversational systems has become critical, as spoken conversational assistants operate in increasingly complex domains. Online satisfaction prediction (i.e., predicting satisfaction of the user with the system after each turn) could be used as a new proxy for implicit user feedback, and offers promising opportunities to create more responsive and effective conversational agents, which adapt to the user's engagement with the agent. To accomplish this goal, we propose a conversational satisfaction prediction model specifically designed for open-domain spoken conversational agents, called ConvSAT. To operate robustly across domains, ConvSAT aggregates multiple representations of the conversation, namely the conversation history, utterance and response content, and system- and user-oriented behavioral signals. We first calibrate ConvSAT performance against state of the art methods on a standard dataset (Dialogue Breakdown Detection Challenge) in an online regime, and then evaluate ConvSAT on a large dataset of conversations with real users, collected as part of the Alexa Prize competition. Our experimental results show that ConvSAT significantly improves satisfaction prediction for both offline and online setting on both datasets, compared to the previously reported state-of-the-art approaches. The insights from our study can enable more intelligent conversational systems, which could adapt in real-time to the inferred user satisfaction and engagement.