Tree-Constrained Graph Neural Networks For Argument Mining

We propose a novel graph neural network architecture inspired by tree kernels, which measure similarity between trees through shared substructures called fragments. Regularization constraints and a dedicated pooling mechanism incorporate fragments into node soft assignments. Experiments on sentence-classification tasks across several argument-mining corpora show competitive performance against state-of-the-art methods.