  Recent NLP literature has seen growing interest in improving model
interpretability. Along this direction, we propose a trainable neural network
layer that learns a global interaction graph between words and then selects
more informative words using the learned word interactions. Our layer, we call
WIGRAPH, can plug into any neural network-based NLP text classifiers right
after its word embedding layer. Across multiple SOTA NLP models and various NLP
datasets, we demonstrate that adding the WIGRAPH layer substantially improves
NLP models' interpretability and enhances models' prediction performance at the
same time.
