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

arXiv: https://arxiv.org/abs/2109.08412 · HTML: https://mlsanity.com/paper/2109.08412/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 42 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 24 of 24)

- [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)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [DialogueRNN: An Attentive RNN for Emotion Detection in Conversations](https://mlsanity.com/paper/1811.00405/) (2018)
- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018)
- [Conversational AI: The Science Behind the Alexa Prize](https://mlsanity.com/paper/1801.03604/) (2018)
- [Dynamic Memory Networks for Visual and Textual Question Answering](https://mlsanity.com/paper/1603.01417/) (2016)
- [How should my chatbot interact? A survey on human-chatbot interaction design](https://mlsanity.com/paper/1904.02743/) (2019)
- [DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification](https://mlsanity.com/paper/2008.06914/) (2020)
- [Dialogue Act Classification with Context-Aware Self-Attention](https://mlsanity.com/paper/1904.02594/) (2019)
- [Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis](https://mlsanity.com/paper/1711.09645/) (2017)
- [Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations](https://mlsanity.com/paper/2010.02495/) (2020)
- [Evaluating Quality of Chatbots and Intelligent Conversational Agents](https://mlsanity.com/paper/1704.04579/) (2017)
- [Dialogue Act Sequence Labeling using Hierarchical encoder with CRF](https://mlsanity.com/paper/1709.04250/) (2017)
- [Multi-domain Conversation Quality Evaluation via User Satisfaction Estimation](https://mlsanity.com/paper/1911.08567/) (2019)
- [Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time](https://mlsanity.com/paper/1801.02668/) (2018)
- [Time to Transfer: Predicting and Evaluating Machine-Human Chatting Handoff](https://mlsanity.com/paper/2012.07610/) (2020)
- [Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use](https://mlsanity.com/paper/1907.07638/) (2019)
- [Domain-Independent turn-level Dialogue Quality Evaluation via User Satisfaction Estimation](https://mlsanity.com/paper/1908.07064/) (2019)
- [Improving Interaction Quality Estimation with BiLSTMs and the Impact on Dialogue Policy Learning](https://mlsanity.com/paper/2001.07615/) (2020)
- [Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue Representation Learning](https://mlsanity.com/paper/2003.04994/) (2020)
- [Local Contextual Attention with Hierarchical Structure for Dialogue Act Recognition](https://mlsanity.com/paper/2003.06044/) (2020)

## Built on by (most cited first, 4 of 4)

- [A System for Human-AI collaboration for Online Customer Support](https://mlsanity.com/paper/2301.12158/) (2023)
- [A Speaker Turn-Aware Multi-Task Adversarial Network for Joint User Satisfaction Estimation and Sentiment Analysis](https://mlsanity.com/paper/2410.09556/) (2024)
- [Causal Inference for Chatting Handoff](https://mlsanity.com/paper/2210.02862/) (2022)
- [Learning When to Automate: Queue Control in Human-AI Service Systems](https://mlsanity.com/paper/2607.06017/) (2026)

## Abstract (arXiv)

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.