  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.

Machine Learning;Computation and Language;Methodology