The larger variance of baselines is due to rationalizing-specific training (Lei et al., 2016), which may cause instability when extracting rationales.
our model utilizes the generality from multi-task learning, which is expected to extract rationales in a more stable manner (lower variance), indicating that the proposed method generalizes to different aspects better than baselines.