Dialog state tracking (DST) suffers from severe data sparsity . In this work, wesuccessfully utilize non-dialog data from unrelated NLP tasks to train dialog state trackers . This opens the door to the abundance of unrelated . NLP . tasks benefit from transfer learning andmulti-task learning, in dialog these methods are limited by the amount of available data and by the specificity of dialog applications .

Author(s) : Michael Heck, Carel van Niekerk, Nurul Lubis, Christian Geishauser, Hsien-Chin Lin, Marco Moresi, Milica Gašić

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Keywords : dialog - state - data - learning - unrelated -

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