Finally, we remark that if we view MRT as a fine tuning step, it can be applied in any joint learning model based on building the joint distribution P (ŷ|s, θ) (e.g., the globally normalized P in (Zhang et al., 2017)).
we would think that MRT is a flexible and lightweight framework for the joint learning.