Dialog Router: Automated Dialog Transition via Multi-Task Learning.
2021
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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