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.
翻译:我们提出了一种解码器Transformer的扩展模型,该模型通过变分方法在无监督条件下学习随机潜变量,并以此调节其生成过程。实验评估表明,允许这种条件调节能显著提升下游任务的性能。