This paper describes our approach in DSTC 8 Track 4: Schema-Guided Dialogue State Tracking. The goal of this task is to predict the intents and slots in each user turn to complete the dialogue state tracking (DST) based on the information provided by the task's schema. Different from traditional stage-wise DST, we propose an end-to-end DST system to avoid error accumulation between the dialogue turns. The DST system consists of a machine reading comprehension (MRC) model for non-categorical slots and a Wide & Deep model for categorical slots. As far as we know, this is the first time that MRC and Wide & Deep model are applied to DST problem in a fully end-to-end way. Experimental results show that our framework achieves an excellent performance on the test dataset including 50% zero-shot services with a joint goal accuracy of 0.8652 and a slot tagging F1-Score of 0.9835.

本文介绍了 DSTC 8 轨道4：基于模式的对话状态跟踪的方法以及测试结果。我们提出了一种端到端的DST系统，该系统包括针对非分类插槽的MRC模型和对于分类插槽的Wide & Deep模型。通过实验，证明我们的方法在测试数据集中得到了优异的性能。

一种具备机器阅读理解和Wide&Deep分类的端到端对话状态追踪系统