Global, model-agnostic machine learning explanation technique for textual data

Abstract

A model-agnostic global explainer for textual data processing (NLP) machine learning (ML) models, 'NLP-MLX', is described herein. NLP-MLX explains global behavior of arbitrary NLP ML models by identifying globally-important tokens within a textual dataset containing text data. NLP-MLX accommodates any arbitrary combination of training dataset pre-processing operations used by the NLP ML model. NLP-MLX includes four main stages. A Text Analysis stage converts text in documents of a target dataset into tokens. A Token Extraction stage uses pre-processing techniques to efficiently pre-filter the complete list of tokens into a smaller set of candidate important tokens. A Perturbation Generation stage perturbs tokens within documents of the dataset to help evaluate the effect of different tokens, and combinations of tokens, on the model's predictions. Finally, a Token Evaluation stage uses the ML model and perturbed documents to evaluate the impact of each candidate token relative to predictions for the original documents.