Out-of-Task Training for Dialog State Tracking Models
Michael C. Heck, Christian Geishauser, Hsien-chin Lin, Nurul Lubis, Marco Moresi, Carel van Niekerk, Milica Gašić · 2020
Dialog state tracking (DST) suffers from severe data sparsity.While many natural language processing (NLP) tasks benefit from transfer learning and multi-task learning, in dialog these methods are limited by the amount of available data and by the specificity of dialog applications.In this work, we successfully utilize non-dialog data from unrelated NLP tasks to train dialog state trackers.This opens the door to the abundance of unrelated NLP corpora to mitigate the data sparsity issue inherent to DST.