Hybrid Dialog State Tracker with ASR Features
Miroslav Vodol\'an, Rudolf Kadlec, Jan Kleindienst

TL;DR
This paper introduces a hybrid dialog state tracker that combines trainable SLU and differentiable rules, achieving state-of-the-art results on the DSTC2 dataset for belief tracking in restaurant dialog systems.
Contribution
A novel hybrid belief tracker integrating trainable SLU with differentiable rules for improved generalization and end-to-end training capabilities.
Findings
Achieved new state-of-the-art results on DSTC2 in three categories.
Demonstrated the effectiveness of combining neural networks with differentiable rules.
Enhanced generalization over purely machine-learning-based belief trackers.
Abstract
This paper presents a hybrid dialog state tracker enhanced by trainable Spoken Language Understanding (SLU) for slot-filling dialog systems. Our architecture is inspired by previously proposed neural-network-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 machine-learning 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 state-of-the-art result in three out of four categories within the DSTC2.
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Taxonomy
TopicsSpeech and dialogue systems · Topic Modeling · Natural Language Processing Techniques
