TIGTEC : Token Importance Guided TExt Counterfactuals
Résumé
Counterfactual examples explain a prediction by highlighting changes in an 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 a 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 modelspecific or model-agnostic way, which makes it very convenient for generating counterfactual explanations.
Domaines
Intelligence artificielle [cs.AI]
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TIGTEC__Token_Importance_Guided_TExt_Counterfactuals___ECML_PKDD_Springer_vlast (1).pdf (406.55 Ko)
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