SIGdial 2016

17th Annual SIGdial Meeting on Discourse and Dialogue

Task Lineages: Dialog State Tracking for Flexible Interaction

Sungjin Lee and Amanda Stent
We consider the gap between user demands for seamless handling of complex interactions, and recent advances in dialog state tracking technologies. We propose a new statistical approach, Task Lineage-based Dialog State Tracking (TL-DST), aimed at seamlessly orchestrating multiple tasks with complex goals across multiple domains in continuous interaction. TL-DST consists of three components: (1) task frame parsing, (2) context fetching and (3) task state update (for which TL-DST takes advantage of previous work in dialog state tracking). There is at present very little publicly available multi-task, complex goal dialog data; however, as a proof of concept, we applied TL-DST to the Dialog State Tracking Challenge (DSTC) 2 data, resulting in state-of-the-art performance. TL-DST also outperforms the DSTC baseline tracker on a set of pseudo-real datasets involving multiple tasks with complex goals which were synthesized using DSTC3 data.