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- W4287605280 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 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." @default.
- W4287605280 created "2022-07-25" @default.
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- W4287605280 date "2020-11-05" @default.
- W4287605280 modified "2023-09-24" @default.
- W4287605280 title "Language Model is All You Need: Natural Language Understanding as Question Answering" @default.
- W4287605280 doi "https://doi.org/10.48550/arxiv.2011.03023" @default.
- W4287605280 hasPublicationYear "2020" @default.
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