An End-to-End Dialogue State Tracking System with Machine Reading Comprehension and Wide & Deep Classification

Understand

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.