616 research outputs found
Capturing Dialogue State Variable Dependencies with an Energy-based Neural Dialogue State Tracker
Dialogue state tracking requires the population and maintenance of a multi-slot frame representation of the dialogue state. Frequently, dialogue state tracking systems assume independence between slot values within a frame. In this paper we argue that treating the prediction of each slot value as an independent prediction task may ignore important associations between the slot values, and, consequently, we argue that treating dialogue state tracking as a structured prediction problem can help to improve dialogue state tracking performance. To support this argument, the research presented in this paper is structured into three stages: (i) analyzing variable dependencies in dialogue data; (ii) applying an energy-based methodology to model dialogue state tracking as a structured prediction task; and (iii) evaluating the impact of inter-slot relationships on model performance. Overall, we demonstrate that modelling the associations between target slots with an energy-based formalism improves dialogue state tracking performance in a number of ways
Scalable Neural Dialogue State Tracking
A Dialogue State Tracker (DST) is a key component in a dialogue system aiming
at estimating the beliefs of possible user goals at each dialogue turn. Most of
the current DST trackers make use of recurrent neural networks and are based on
complex architectures that manage several aspects of a dialogue, including the
user utterance, the system actions, and the slot-value pairs defined in a
domain ontology. However, the complexity of such neural architectures incurs
into a considerable latency in the dialogue state prediction, which limits the
deployments of the models in real-world applications, particularly when task
scalability (i.e. amount of slots) is a crucial factor. In this paper, we
propose an innovative neural model for dialogue state tracking, named Global
encoder and Slot-Attentive decoders (G-SAT), which can predict the dialogue
state with a very low latency time, while maintaining high-level performance.
We report experiments on three different languages (English, Italian, and
German) of the WoZ2.0 dataset, and show that the proposed approach provides
competitive advantages over state-of-art DST systems, both in terms of accuracy
and in terms of time complexity for predictions, being over 15 times faster
than the other systems.Comment: 8 pages, 3 figures, Accepted at ASRU 201
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