# A Speaker Turn-Aware Multi-Task Adversarial Network for Joint User Satisfaction Estimation and Sentiment Analysis

arXiv: https://arxiv.org/abs/2410.09556 · HTML: https://mlsanity.com/paper/2410.09556/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 12 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [DialogueRNN: An Attentive RNN for Emotion Detection in Conversations](https://mlsanity.com/paper/1811.00405/) (2018)
- [Simulating User Satisfaction for the Evaluation of Task-oriented Dialogue Systems](https://mlsanity.com/paper/2105.03748/) (2021)
- [Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification](https://mlsanity.com/paper/2012.13260/) (2020)
- [DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification](https://mlsanity.com/paper/2008.06914/) (2020)
- [Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis](https://mlsanity.com/paper/1711.09645/) (2017)
- [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)
- [Meta Multi-Task Learning for Sequence Modeling](https://mlsanity.com/paper/1802.08969/) (2018)
- [Speaker Turn Modeling for Dialogue Act Classification](https://mlsanity.com/paper/2109.05056/) (2021)
- [Multi-task dialog act and sentiment recognition on Mastodon](https://mlsanity.com/paper/1807.05013/) (2018)
- [Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents](https://mlsanity.com/paper/2010.11230/) (2020)
- [Time to Transfer: Predicting and Evaluating Machine-Human Chatting Handoff](https://mlsanity.com/paper/2012.07610/) (2020)
- [A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis](https://mlsanity.com/paper/2109.08412/) (2021)

## Built on by (most cited first, 2 of 2)

- [A benchmark for joint dialogue satisfaction, emotion recognition, and emotion state transition prediction](https://mlsanity.com/paper/2603.03327/) (2026)
- [Seeking Information with RAG-Assistants: Does Model Size Matter in Human-AI Collaborations?](https://mlsanity.com/paper/2605.00964/) (2026)

## Abstract (arXiv)

User Satisfaction Estimation is an important task and increasingly being applied in goal-oriented dialogue systems to estimate whether the user is satisfied with the service. It is observed that whether the user's needs are met often triggers various sentiments, which can be pertinent to the successful estimation of user satisfaction, and vice versa. Thus, User Satisfaction Estimation (USE) and Sentiment Analysis (SA) should be treated as a joint, collaborative effort, considering the strong connections between the sentiment states of speakers and the user satisfaction. Existing joint learning frameworks mainly unify the two highly pertinent tasks over cascade or shared-bottom implementations, however they fail to distinguish task-specific and common features, which will produce sub-optimal utterance representations for downstream tasks. In this paper, we propose a novel Speaker Turn-Aware Multi-Task Adversarial Network (STMAN) for dialogue-level USE and utterance-level SA. Specifically, we first introduce a multi-task adversarial strategy which trains a task discriminator to make utterance representation more task-specific, and then utilize a speaker-turn aware multi-task interaction strategy to extract the common features which are complementary to each task. Extensive experiments conducted on two real-world service dialogue datasets show that our model outperforms several state-of-the-art methods.