The Dialog State Tracking Challenge Series

Williams, Jason D. (Microsoft Corporation) | Henderson, Matthew (Cambridge University) | Raux, Antoine (Lenovo Labs) | Thomson, Blaise (VocalIQ, Ltd) | Black, Alan (Carnegie Mellon University) | Ramachandran, Deepak (Nuance Communications, Inc.)

AI Magazine 

Dialog state tracking is difficult because automatic speech recognition (ASR) and spoken language understanding (SLU) errors are common and can cause the system to misunderstand the user. At the same time, state tracking is crucial because the system relies on the estimated dialog state to choose actions -- for example, which restaurants to suggest. Figure 1 shows an illustration of the dialog state tracking task. Historically dialog state tracking has been done with handcrafted rules. More recently, statistical methods have been found to be superior by effectively overcoming some SLU errors, resulting in better dialogs. Despite this progress, direct comparisons between methods have not been possible because past studies use different domains, system components, and evaluation measures, hindering progresss.

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