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Recent NLP literature has seen growing interest in improving modelinterpretability. Along this direction, we propose a trainable neural networklayer that learns a global interaction graph between words and then selectsmore informative words using the learned word interactions. Our layer, we callWIGRAPH, can plug into any neural network-based NLP text classifiers rightafter its word embedding layer. Across multiple SOTA NLP models and various NLPdatasets, we demonstrate that adding the WIGRAPH layer substantially improvesNLP models' interpretability and enhances models' prediction performance at thesame time.

Improving Interpretability via Explicit Word Interaction Graph Layer