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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Colin Raffel
Michael Matena
Noam Shazeer
Sharan Narang
Wei Li
Google (2019)
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Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a lower-resource downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning for NLP by introducing a unified framework which casts every language problem as a text-to-text task. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of text understanding tasks. By combining the insights gained in our exploration with scale and a new giant unlabeled text dataset, we achieve state-of-the-art results in most of the tasks we consider. To facilitate future work on text understanding, we release our dataset, pre-trained models, and code.