Dialog Router: Automated Dialog Transition via Multi-Task Learning

Authors

  • Ziming Huang IBM Research-China, Beijing, China
  • Zhuoxuan Jiang JD AI Research, Shanghai, China
  • Hao Chen IBM Research-China, Beijing, China
  • Xue Han IBM Research-China, Beijing, China
  • Yabin Dang IBM Research-China, Beijing, China

Keywords:

Dialog Router, Dialog Orchestration, Dialog Classification, Dialog Regression, Multi-task Learning

Abstract

Dialog Router is a general paradigm for human-bot symbiosis dialog systems to provide friendly customer care service. It is equipped with a multi-task learning model to automatically capture the underlying correlation between multiple related tasks, i.e. dialog classification and regression, and greatly reduce human labor work for system customization, which improves the accuracy of dialog transition. In addition, for learning the multi-task model, the training data and labels are easy to collect from human-to-human historical dialog logs, and the Dialog Router can be easily integrated into the majority of existing dialog systems by calling general APIs. We conduct experiments on real-world datasets for dialog classification and regression. The results show that our model achieves improvements on both tasks, which benefits the dialog transition application. The demo illustrates our method’s effectiveness in a real customer care service.

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Published

2021-05-18

How to Cite

Huang, Z., Jiang, Z., Chen, H., Han, X., & Dang, Y. (2021). Dialog Router: Automated Dialog Transition via Multi-Task Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 16038-16040. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/18005