Optimal and efficient text counterfactuals using Graph Neural Networks

• URL: http://arxiv.org/abs/2408.01969v2
• Date: Sun, 6 Oct 2024 12:00:55 GMT
• Title: Optimal and efficient text counterfactuals using Graph Neural Networks
• Authors: Dimitris Lymperopoulos, Maria Lymperaiou, Giorgos Filandrianos, Giorgos Stamou,
• Abstract summary: We propose a framework that achieves the aforementioned by generating semantically edited inputs, known as counterfactual interventions. We test our framework on two NLP tasks - binary sentiment classification and topic classification - and show that the generated edits are contrastive, fluent and minimal.
• Score: 1.9939549451457024
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As NLP models become increasingly integral to decision-making processes, the need for explainability and interpretability has become paramount. In this work, we propose a framework that achieves the aforementioned by generating semantically edited inputs, known as counterfactual interventions, which change the model prediction, thus providing a form of counterfactual explanations for the model. We test our framework on two NLP tasks - binary sentiment classification and topic classification - and show that the generated edits are contrastive, fluent and minimal, while the whole process remains significantly faster that other state-of-the-art counterfactual editors.

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