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.

翻译：当前，人机共生对话系统（例如电子商务中的售前售后场景）已广泛应用，而对话路由组件对于提升整体效率、降低人力资源成本及优化用户体验至关重要。尽管现有方法大多能满足这一需求，但它们仅能建模单源对话数据，无法有效捕捉数据间及子任务间的潜在关联知识。本文通过深入挖掘各类对话数据中的"数据-任务"与"任务-任务"知识，系统研究了这一重要问题。为实现上述目标，我们提出了一种门控机制增强的多任务模型（Gated Mechanism enhanced Multi-task Model, G3M），具体包含新型对话编码器及两个定制化门控机制模块。所提方法可实现层级化信息过滤，且对现有对话系统具有非侵入性。基于两套真实场景数据集的实验结果表明，本方法在RMSE指标上提升8.7%/11.8%、F1指标上提升2.2%/4.4%，取得了当前最优性能。