Hybrid Dialog State Tracker with ASR Features
نویسندگان
چکیده
This paper presents a hybrid dialog state tracker enhanced by trainable Spoken Language Understanding (SLU) for slotfilling dialog systems. Our architecture is inspired by previously proposed neuralnetwork-based belief-tracking systems. In addition we extended some parts of our modular architecture with differentiable rules to allow end-to-end training. We hypothesize that these rules allow our tracker to generalize better than pure machinelearning based systems. For evaluation we used the Dialog State Tracking Challenge (DSTC) 2 dataset a popular belief tracking testbed with dialogs from restaurant information system. To our knowledge, our hybrid tracker sets a new stateof-the-art result in three out of four categories within the DSTC2.
منابع مشابه
Hybrid Dialog State Tracker
This paper presents a hybrid dialog state tracker that combines a rule based and a machine learning based approach to belief state tracking. Therefore, we call it a hybrid tracker. The machine learning in our tracker is realized by a Long Short Term Memory (LSTM) network. To our knowledge, our hybrid tracker sets a new state-of-the-art result for the Dialog State Tracking Challenge (DSTC) 2 dat...
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