An End-to-End Dialogue State Tracking System with Machine Reading Comprehension and Wide & Deep Classification
This work addresses dialogue state tracking for conversational AI systems, presenting an incremental improvement by integrating existing methods in a novel way for the task.
The paper tackles the problem of dialogue state tracking by proposing an end-to-end system that combines machine reading comprehension and Wide & Deep classification to avoid error accumulation, achieving a joint goal accuracy of 0.8652 and slot tagging F1-Score of 0.9835 on a test dataset with 50% zero-shot services.
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.