Language Model is all You Need: Natural Language Understanding as Question Answering

Abstract

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 certain family of transfer learning, where the target domain is mapped to the source domain. Specifically we map Natural Language Understanding (NLU) problems to Question Answering (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.