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Out-of-Task Training for Dialog State Tracking Models

2020-11-18 16:23:30
Michael Heck, Carel van Niekerk, Nurul Lubis, Christian Geishauser, Hsien-Chin Lin, Marco Moresi, Milica Gašić
arXiv_CL
         
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2011.09379

PDF

https://arxiv.org/pdf/2011.09379.pdf

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