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Don extbf{Abstract} ewline Modern methods in the machine learning field are data-intensive, requiring large labeled datasets for their training and optimal performance. However, large amounts of data for some languages and domains are not available. Self-supervised models can be used to train universal representations, and they are beneficial for transfer learning in downstream tasks.

Transformer-based models have been successfully applied in various natural language processing tasks. Current transformer-based models rely on large labeled datasets to achieve state-of-the-art performance. Transformer encoder-decoder models are mostly used for sequence-to-sequence tasks.

ewline We propose a new self-supervised approach, extbf{TransER}, which is an encoder-decoder transformer-based architecture trained to learn universal representations. Our proposed model is trained to reconstruct corrupted input and output pairs. The model learns both encoder and decoder parameters from unlabeled data to enable knowledge transfer for downstream tasks.