  Various approaches have been proposed for providing efficient computational
approaches for abstract argumentation. Among them, neural networks have
permitted to solve various decision problems, notably related to arguments
(credulous or skeptical) acceptability. In this work, we push further this
study in various ways. First, relying on the state-of-the-art approach AFGCN,
we show how we can improve the performances of the Graph Convolutional Networks
(GCNs) regarding both runtime and accuracy. Then, we show that it is possible
to improve even more the efficiency of the approach by modifying the
architecture of the network, using Graph Attention Networks (GATs) instead.
