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arXiv · 2304.12425

TIGTEC : Token Importance Guided TExt Counterfactuals

Milan Bhan·Jean-Noel Vittaut·Nicolas Chesneau·Marie-Jeanne Lesot
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Abstract

Counterfactual examples explain a prediction by highlighting changes of instance that flip the outcome of a classifier. This paper proposes TIGTEC, an efficient and modular method for generating sparse, plausible and diverse counterfactual explanations for textual data. TIGTEC is a text editing heuristic that targets and modifies words with high contribution using local feature importance. A new attention-based local feature importance is proposed. Counterfactual candidates are generated and assessed with a cost function integrating semantic distance, while the solution space is efficiently explored in a beam search fashion. The conducted experiments show the relevance of TIGTEC in terms of success rate, sparsity, diversity and plausibility. This method can be used in both model-specific or model-agnostic way, which makes it very convenient for generating counterfactual explanations.

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Milan Bhan, Jean-Noel Vittaut, Nicolas Chesneau, Marie-Jeanne Lesot. 2023-04-24. TIGTEC : Token Importance Guided TExt Counterfactuals. https://arxiv.org/abs/2304.12425

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