# Modeling User Satisfaction Dynamics in Dialogue via Hawkes Process

arXiv: https://arxiv.org/abs/2305.12594 · HTML: https://mlsanity.com/paper/2305.12594/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 51 references that are in the map.
- First posted on arXiv 21 May 2023.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/1909.05855/) (2019)
- [Transformer Hawkes Process](https://mlsanity.com/paper/2002.09291/) (2020)
- [Why We Need New Evaluation Metrics for NLG](https://mlsanity.com/paper/1707.06875/) (2017)
- [Towards Deep Conversational Recommendations](https://mlsanity.com/paper/1812.07617/) (2018)
- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Survey on Evaluation Methods for Dialogue Systems](https://mlsanity.com/paper/1905.04071/) (2019)
- [Learning from Dialogue after Deployment: Feed Yourself, Chatbot!](https://mlsanity.com/paper/1901.05415/) (2019)
- [Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation](https://mlsanity.com/paper/1807.01473/) (2018)
- [Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks](https://mlsanity.com/paper/1705.08982/) (2017)
- [The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service](https://mlsanity.com/paper/1911.09969/) (2019)
- [Simulating User Satisfaction for the Evaluation of Task-oriented Dialogue Systems](https://mlsanity.com/paper/2105.03748/) (2021)
- [HiGRU: Hierarchical Gated Recurrent Units for Utterance-level Emotion Recognition](https://mlsanity.com/paper/1904.04446/) (2019)
- [Neural Point Process for Learning Spatiotemporal Event Dynamics](https://mlsanity.com/paper/2112.06351/) (2021)
- [Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification](https://mlsanity.com/paper/2012.13260/) (2020)
- [User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems](https://mlsanity.com/paper/2202.02912/) (2022)
- [Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations](https://mlsanity.com/paper/2010.02495/) (2020)
- [Multi-task dialog act and sentiment recognition on Mastodon](https://mlsanity.com/paper/1807.05013/) (2018)
- [Learning Hawkes Processes from a Handful of Events](https://mlsanity.com/paper/1911.00292/) (2019)
- [Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents](https://mlsanity.com/paper/2010.11230/) (2020)
- [Multi-domain Conversation Quality Evaluation via User Satisfaction Estimation](https://mlsanity.com/paper/1911.08567/) (2019)
- [Offline and Online Satisfaction Prediction in Open-Domain Conversational Systems](https://mlsanity.com/paper/2006.01921/) (2020)
- [Large-scale Hybrid Approach for Predicting User Satisfaction with Conversational Agents](https://mlsanity.com/paper/2006.07113/) (2020)

## Built on by (most cited first, 6 of 6)

- [Towards Human-centered Proactive Conversational Agents](https://mlsanity.com/paper/2404.12670/) (2024)
- [Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue System](https://mlsanity.com/paper/2306.09821/) (2023)
- [CAUSE: Counterfactual Assessment of User Satisfaction Estimation in Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2403.19056/) (2024)
- [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)
- [A benchmark for joint dialogue satisfaction, emotion recognition, and emotion state transition prediction](https://mlsanity.com/paper/2603.03327/) (2026)

## Abstract (arXiv)

Dialogue systems have received increasing attention while automatically evaluating their performance remains challenging. User satisfaction estimation (USE) has been proposed as an alternative. It assumes that the performance of a dialogue system can be measured by user satisfaction and uses an estimator to simulate users. The effectiveness of USE depends heavily on the estimator. Existing estimators independently predict user satisfaction at each turn and ignore satisfaction dynamics across turns within a dialogue. In order to fully simulate users, it is crucial to take satisfaction dynamics into account. To fill this gap, we propose a new estimator ASAP (sAtisfaction eStimation via HAwkes Process) that treats user satisfaction across turns as an event sequence and employs a Hawkes process to effectively model the dynamics in this sequence. Experimental results on four benchmark dialogue datasets demonstrate that ASAP can substantially outperform state-of-the-art baseline estimators.