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.

翻译：已有多种方法被提出用于提供抽象论证的高效计算方法。其中，神经网络已成功解决多种决策问题，尤其与论证（轻信性或怀疑性）可接受性相关。在本研究中，我们通过多种方式进一步推进该研究。首先，基于当前最先进的方法AFGCN，我们展示了如何提升图卷积网络（GCNs）在运行时间与准确性两方面的性能。其次，我们证明通过采用图注意力网络（GATs）替代原有网络架构，能够进一步提升该方法的效率。