A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis
Jiawei Liu, Kaisong Song, Yangyang Kang, Guoxiu He, Zhuoren Jiang,, Changlong Sun, Wei Lu, Xiaozhong Liu

TL;DR
This paper introduces the Role-Selected Sharing Network (RSSN), a multi-task model that jointly predicts chatbot failure and assesses user satisfaction to improve machine-human chat handoff decisions.
Contribution
The novel RSSN model effectively combines satisfaction estimation and handoff prediction using role-based sharing, enhancing dialogue management in chatbots.
Findings
RSSN outperforms existing models on public datasets.
Effective exchange of information between tasks improves handoff accuracy.
Role-based decoupling enhances model interpretability and performance.
Abstract
Chatbot is increasingly thriving in different domains, however, because of unexpected discourse complexity and training data sparseness, its potential distrust hatches vital apprehension. Recently, Machine-Human Chatting Handoff (MHCH), predicting chatbot failure and enabling human-algorithm collaboration to enhance chatbot quality, has attracted increasing attention from industry and academia. In this study, we propose a novel model, Role-Selected Sharing Network (RSSN), which integrates both dialogue satisfaction estimation and handoff prediction in one multi-task learning framework. Unlike prior efforts in dialog mining, by utilizing local user satisfaction as a bridge, global satisfaction detector and handoff predictor can effectively exchange critical information. Specifically, we decouple the relation and interaction between the two tasks by the role information after the shared…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopic Modeling · Speech and dialogue systems · AI in Service Interactions
