A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis

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 encoder. Extensive experiments on two public datasets demonstrate the effectiveness of our model.