For example, ASP with three-source tasks achieves 82.23% and 66.92% accuracy, respectively, in SNLI and MNLI, which are lower than 82.28% and 67.39% accuracy with its best performance with two-source tasks.
tARS overcomes such challenges, for example, 83.12% > 82.67% and 68.24% > 67.79% in SNLI and MNLI, except for QQP, which can be further improved by asymmetric MtL techniques (Lee et al., 2016).