Auto-sizing improves machine translation efficiency by 3.9 BLEU points with fewer parameters.
problem Optimizing neural network architecture and hyperparameters for low-resource machine translation is computationally expensive.
method Auto-sizing uses regularization to dynamically adjust network size during training.
result Auto-sizing improves BLEU scores by up to 3.9 points with one-third fewer parameters.