Improving Interpretability via Explicit Word Interaction Graph Layer

• URL: http://arxiv.org/abs/2302.02016v1
• Date: Fri, 3 Feb 2023 21:56:32 GMT
• Title: Improving Interpretability via Explicit Word Interaction Graph Layer
• Authors: Arshdeep Sekhon, Hanjie Chen, Aman Shrivastava, Zhe Wang, Yangfeng Ji, Yanjun Qi
• Abstract summary: We propose a trainable neural network layer that learns a global interaction graph between words and then selects more informative words. Our layer, we call WIGRAPH, can plug into any neural network-based NLP text classifiers right after its word embedding layer.
• Score: 28.28660926203816
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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