# Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents

arXiv: https://arxiv.org/abs/2010.11230 · HTML: https://mlsanity.com/paper/2010.11230/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

## Built on by (most cited first, 13 of 13)

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems](https://mlsanity.com/paper/2202.02912/) (2022)
- [Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models](https://mlsanity.com/paper/2403.12388/) (2024)
- [Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues](https://mlsanity.com/paper/2305.16798/) (2023)
- [Modeling User Satisfaction Dynamics in Dialogue via Hawkes Process](https://mlsanity.com/paper/2305.12594/) (2023)
- [Scalable and Robust Self-Learning for Skill Routing in Large-Scale Conversational AI Systems](https://mlsanity.com/paper/2204.07135/) (2022)
- [Constrained Policy Optimization for Controlled Self-Learning in Conversational AI Systems](https://mlsanity.com/paper/2209.08429/) (2022)
- [DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling](https://mlsanity.com/paper/2204.07679/) (2022)
- [Domain-Aware Contrastive Knowledge Transfer for Multi-domain Imbalanced Data](https://mlsanity.com/paper/2204.01916/) (2022)
- [A Speaker Turn-Aware Multi-Task Adversarial Network for Joint User Satisfaction Estimation and Sentiment Analysis](https://mlsanity.com/paper/2410.09556/) (2024)
- [LLM-guided Plan and Retrieval: A Strategic Alignment for Interpretable User Satisfaction Estimation in Dialogue](https://mlsanity.com/paper/2503.04675/) (2025)
- [Scalable and Safe Remediation of Defective Actions in Self-Learning Conversational Systems](https://mlsanity.com/paper/2305.10528/) (2023)
- [Data Augmentation for Improving Tail-traffic Robustness in Skill-routing for Dialogue Systems](https://mlsanity.com/paper/2306.04823/) (2023)

## Abstract (arXiv)

Turn-level user satisfaction is one of the most important performance metrics for conversational agents. It can be used to monitor the agent's performance and provide insights about defective user experiences. Moreover, a powerful satisfaction model can be used as an objective function that a conversational agent continuously optimizes for. While end-to-end deep learning has shown promising results, having access to a large number of reliable annotated samples required by these methods remains challenging. In a large-scale conversational system, there is a growing number of newly developed skills, making the traditional data collection, annotation, and modeling process impractical due to the required annotation costs as well as the turnaround times. In this paper, we suggest a self-supervised contrastive learning approach that leverages the pool of unlabeled data to learn user-agent interactions. We show that the pre-trained models using the self-supervised objective are transferable to the user satisfaction prediction. In addition, we propose a novel few-shot transfer learning approach that ensures better transferability for very small sample sizes. The suggested few-shot method does not require any inner loop optimization process and is scalable to very large datasets and complex models. Based on our experiments using real-world data from a large-scale commercial system, the suggested approach is able to significantly reduce the required number of annotations, while improving the generalization on unseen out-of-domain skills.