KERMITviz: Visualizing Neural Network Activations on Syntactic Trees

The study of symbolic syntactic interpretations has been the cornerstone of natural language understanding for many years. Today, modern artificial neural networks are widely searched to assess their syntactic ability, through several probing tasks. In this paper, we propose a neural network system that explicitly includes syntactic interpretations: Kernel-inspired Encoder with Recursive Mechanism for Interpretable Trees Visualizer (KERMITviz). The most important result is that KERMITviz allows to visualize how syntax is used in inference. This system can be used in combination with transformer architectures like BERT, XLNet and clarifies the use of symbolic syntactic interpretations in specific neural networks making the black-box neural network neural networks explainable, interpretable and clear.