@inproceedings{liu-etal-2021-role,
title = "A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis",
author = "Liu, Jiawei and
Song, Kaisong and
Kang, Yangyang and
He, Guoxiu and
Jiang, Zhuoren and
Sun, Changlong and
Lu, Wei and
Liu, Xiaozhong",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.767/",
doi = "10.18653/v1/2021.emnlp-main.767",
pages = "9731--9741",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis
%A Liu, Jiawei
%A Song, Kaisong
%A Kang, Yangyang
%A He, Guoxiu
%A Jiang, Zhuoren
%A Sun, Changlong
%A Lu, Wei
%A Liu, Xiaozhong
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F liu-etal-2021-role
%X 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.
%R 10.18653/v1/2021.emnlp-main.767
%U https://aclanthology.org/2021.emnlp-main.767/
%U https://doi.org/10.18653/v1/2021.emnlp-main.767
%P 9731-9741
Markdown (Informal)
[A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis](https://aclanthology.org/2021.emnlp-main.767/) (Liu et al., EMNLP 2021)
ACL
- Jiawei Liu, Kaisong Song, Yangyang Kang, Guoxiu He, Zhuoren Jiang, Changlong Sun, Wei Lu, and Xiaozhong Liu. 2021. A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9731–9741, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.