Autoencoding Transformers

T5, or Text-to-Text Transfer Transformer, is a Transformer based architecture that uses a text-to-text approach. Every task – including translation, question answering, and classification – is cast as feeding the model text as input and training it to generate some target text. This allows for the use of the same model, loss function, hyperparameters, etc. across our diverse set of tasks. The changes compared to BERT include:

  • adding a causal decoder to the bidirectional architecture.
  • replacing the fill-in-the-blank cloze task with a mix of alternative pre-training tasks.
Source: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

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