Transparent Neighborhood Approximation for Text Classifier Explanation

TL;DR

This paper introduces XPROB, a transparent, probability-based method for generating neighboring texts to explain classifiers, improving interpretability and stability over neural network generators.

Contribution

It proposes a novel, fully transparent editing approach for neighborhood construction in text classifier explanations, replacing opaque neural generators.

Findings

XPROB achieves competitive explanation performance.

XPROB offers higher transparency and controllability.

XPROB demonstrates superior stability in explanations.

Abstract

Recent literature highlights the critical role of neighborhood construction in deriving model-agnostic explanations, with a growing trend toward deploying generative models to improve synthetic instance quality, especially for explaining text classifiers. These approaches overcome the challenges in neighborhood construction posed by the unstructured nature of texts, thereby improving the quality of explanations. However, the deployed generators are usually implemented via neural networks and lack inherent explainability, sparking arguments over the transparency of the explanation process itself. To address this limitation while preserving neighborhood quality, this paper introduces a probability-based editing method as an alternative to black-box text generators. This approach generates neighboring texts by implementing manipulations based on in-text contexts. Substituting the generator-based construction process with recursive probability-based editing, the resultant explanation method, XPROB (explainer with probability-based editing), exhibits competitive performance according to the evaluation conducted on two real-world datasets. Additionally, XPROB's fully transparent and more controllable construction process leads to superior stability compared to the generator-based explainers.