An End-to-end Approach for Handling Unknown Slot Values in Dialogue State Tracking
This addresses a practical issue in dialogue systems for improving robustness in real-world applications, though it is incremental as it builds on existing pointer network methods.
The paper tackles the problem of handling unknown slot values in dialogue state tracking, which previous approaches often ignore, and presents an end-to-end pointer network architecture that achieves state-of-the-art accuracy on the DSTC2 benchmark while effectively extracting unknown values.
We highlight a practical yet rarely discussed problem in dialogue state tracking (DST), namely handling unknown slot values. Previous approaches generally assume predefined candidate lists and thus are not designed to output unknown values, especially when the spoken language understanding (SLU) module is absent as in many end-to-end (E2E) systems. We describe in this paper an E2E architecture based on the pointer network (PtrNet) that can effectively extract unknown slot values while still obtains state-of-the-art accuracy on the standard DSTC2 benchmark. We also provide extensive empirical evidence to show that tracking unknown values can be challenging and our approach can bring significant improvement with the help of an effective feature dropout technique.