In this section, we study the aggregation function φ with a routing scheme, which has achieved good results in other NLP tasks (Zhao et al., , 2019.
we introduce a nonparametric clustering with Kernel Density Estimation (KDE) for routing since KDE bridges a family of kernel functions with underlying empirical distributions, which often leads to computational efficiency , defined as: where d(•) is a distance function, γ ij denotes the underlying closeness between the aggregated vector v j and vector z i in the i-th layer, and k is a kernel function.