About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
EACL 2017
Conference paper
Hybrid dialog state tracker with ASR features
Abstract
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.