MixLoss: A Data Augmentation Method for Sequence Generation Tasks
Journal: Research Square
Year: 2022

Abstract In this paper, we propose MixLoss, a simple and effective data augmentation method for sequence generation tasks like machine translation, speech recognition, etc. MixLoss works by mixing two inputs and two losses with two corresponding outputs using the same weights, which can also be applied in the hidden space. Concretely, when the loss takes the certain form that we define, we prove that MixLoss is theoretically equivalent to Mixup and thus has an adequate theoretical foundation. We