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Task-oriented dialog systems rely on dialog state tracking (DST) to monitorthe user's goal during the course of an interaction. Multi-domain andopen-vocabulary settings complicate the task considerably and demand scalablesolutions. In this paper we present a new approach to DST which makes use ofvarious copy mechanisms to fill slots with values. Our model has no need tomaintain a list of candidate values. Instead, all values are extracted from thedialog context on-the-fly. A slot is filled by one of three copy mechanisms:(1) Span prediction may extract values directly from the user input; (2) avalue may be copied from a system inform memory that keeps track of thesystem's inform operations; (3) a value may be copied over from a differentslot that is already contained in the dialog state to resolve coreferenceswithin and across domains. Our approach combines the advantages of span-basedslot filling methods with memory methods to avoid the use of value picklistsaltogether. We argue that our strategy simplifies the DST task while at thesame time achieving state of the art performance on various popular evaluationsets including Multiwoz 2.1, where we achieve a joint goal accuracy beyond 55%.

TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking