Abstract
Extending the decoder Transformer with unsupervised learned random latent variables enhances performance on downstream tasks.
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We propose an extension of the decoder Transformer that conditions its generative process on random latent variables which are learned without supervision thanks to a variational procedure. Experimental evaluations show that allowing such a conditioning translates into substantial improvements on downstream tasks.
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