Tricks for Training Sparse Translation Models

Abstract

Multi-task learning with an unbalanced data distribution skews learning towards high resource tasks, especially when model capacity is fixed and fully shared across all tasks. Sparse scaling architectures, such as BASELayers, provide flexible mechanisms for tasks to have a variable number of parameters, which can be useful to counterbalance skewed data distributions. However, we find that that BASELayers sparse model for multilingual machine translation can perform poorly out of the box, and propose two straightforward techniques to mitigate this - a temperature heating mechanism and dense pre-training. Overall, these methods improve performance on two multilingual translation benchmarks compared to standard BASELayers and dense scaling baselines, and in combination, more than 2x model convergence speed.