Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking
This work addresses the need for interpretability in GNNs for NLP, which is crucial for researchers and practitioners to understand model decisions, though it is incremental as it builds on existing GNN methods.
The authors tackled the problem of interpreting Graph Neural Networks (GNNs) in NLP by developing a post-hoc method that identifies unnecessary edges in graphs, such as syntactic trees, and demonstrated that a large proportion of edges can be dropped without performance loss in tasks like question answering and semantic role labeling.
Graph neural networks (GNNs) have become a popular approach to integrating structural inductive biases into NLP models. However, there has been little work on interpreting them, and specifically on understanding which parts of the graphs (e.g. syntactic trees or co-reference structures) contribute to a prediction. In this work, we introduce a post-hoc method for interpreting the predictions of GNNs which identifies unnecessary edges. Given a trained GNN model, we learn a simple classifier that, for every edge in every layer, predicts if that edge can be dropped. We demonstrate that such a classifier can be trained in a fully differentiable fashion, employing stochastic gates and encouraging sparsity through the expected $L_0$ norm. We use our technique as an attribution method to analyze GNN models for two tasks -- question answering and semantic role labeling -- providing insights into the information flow in these models. We show that we can drop a large proportion of edges without deteriorating the performance of the model, while we can analyse the remaining edges for interpreting model predictions.