  We propose a novel architecture for Graph Neural Networks that is inspired by
the idea behind Tree Kernels of measuring similarity between trees by taking
into account their common substructures, named fragments. By imposing a series
of regularization constraints to the learning problem, we exploit a pooling
mechanism that incorporates such notion of fragments within the node soft
assignment function that produces the embeddings. We present an extensive
experimental evaluation on a collection of sentence classification tasks
conducted on several argument mining corpora, showing that the proposed
approach performs well with respect to state-of-the-art techniques.
