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Multi-domain Dialog State Tracking using Recurrent Neural Networks
2015
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
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Dialog state tracking is a key component of many modern dialog systems, most of which are designed with a single, welldefined domain in mind. This paper shows that dialog data drawn from different dialog domains can be used to train a general belief tracking model which can operate across all of these domains, exhibiting superior performance to each of the domainspecific models. We propose a training procedure which uses out-of-domain data to initialise belief tracking models for entirely new
doi:10.3115/v1/p15-2130
dblp:conf/acl/MrksicSTGSVWY15
fatcat:zic6tg2c7jfpjdro347ybectxm