Currently, human-bot symbiosis dialog systems, e.g., pre- and after-sales in
E-commerce, are ubiquitous, and the dialog routing component is essential to
improve the overall efficiency, reduce human resource cost, and enhance user
experience. Although most existing methods can fulfil this requirement, they
can only model single-source dialog data and cannot effectively capture the
underlying knowledge of relations among data and subtasks. In this paper, we
investigate this important problem by thoroughly mining both the data-to-task
and task-to-task knowledge among various kinds of dialog data. To achieve the
above targets, we propose a Gated Mechanism enhanced Multi-task Model (G3M),
specifically including a novel dialog encoder and two tailored gated mechanism
modules. The proposed method can play the role of hierarchical information
filtering and is non-invasive to existing dialog systems. Based on two datasets
collected from real world applications, extensive experimental results
demonstrate the effectiveness of our method, which achieves the
state-of-the-art performance by improving 8.7\%/11.8\% on RMSE metric and
2.2\%/4.4\% on F1 metric.

本文提出一种基于 Gated Mechanism 的多任务模型来处理各种对话数据之间的任务和知识关系，并取得了最优表现。