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Feature attribution a.k.a. input salience methods which assign an importancescore to a feature are abundant but may produce surprisingly different resultsfor the same model on the same input. While differences are expected ifdisparate definitions of importance are assumed, most methods claim to providefaithful attributions and point at the features most relevant for a model'sprediction. Existing work on faithfulness evaluation is not conclusive and doesnot provide a clear answer as to how different methods are to be compared.Focusing on text classification and the model debugging scenario, our maincontribution is a protocol for faithfulness evaluation that makes use ofpartially synthetic data to obtain ground truth for feature importance ranking.Following the protocol, we do an in-depth analysis of four standard saliencemethod classes on a range of datasets and shortcuts for BERT and LSTM modelsand demonstrate that some of the most popular method configurations providepoor results even for simplest shortcuts. We recommend following the protocolfor each new task and model combination to find the best method for identifyingshortcuts.

"Will You Find These Shortcuts?" A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification