Before revision:  Experiments on TED, WMT and OPUS-100 benchmarks demonstrate that CATS advances the Pareto front of accuracy while being efficient to apply to massive multilingual settings at the scale of 100 languages.
After revision:  Experiments on TED, WMT and OPUS-100 ) with varying degrees of data imbalance. CATS effectively improved multilingual optimization and as a result demonstrated consistent gains on low resources ( to BLEU) without hurting high resources. In addition, CATS is robust to overparameterization and large batch size training, making it a promising training method for massive multilingual models that truly improve low resource languages.