  Different flavors of transfer learning have shown tremendous impact in
advancing research and applications of machine learning. In this work we study
the use of a specific family of transfer learning, where the target domain is
mapped to the source domain. Specifically we map Natural Language Understanding
(NLU) problems to QuestionAnswering (QA) problems and we show that in low data
regimes this approach offers significant improvements compared to other
approaches to NLU. Moreover we show that these gains could be increased through
sequential transfer learning across NLU problems from different domains. We
show that our approach could reduce the amount of required data for the same
performance by up to a factor of 10.
