To demonstrate the effectiveness and flexibility of our soft-coding approaches, we evaluate TARS on five different datasets covering diverse scenarios and CASE on cross-lingual natural language inference (XNLI) datasets with 15 languages (including low-resource language such as Swahili and Urdu), and show that TARS and CASE outperform existing hard-coding approaches.
trying to keep the shared space too pure inevitably leads to sparseness, for which we additionally introduce the density constraint L dense for this purpose.