Abstract
We propose an approach to faithfully explaining text classification models, using a specifically designed neural network to find explanations in the form of machine-annotated rationales during the prediction process. This results in faithful explanations that are similar to human-annotated rationales, while not requiring human explanation examples during training. The quality
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