Evaluating Explanations for Reading Comprehension with Realistic Counterfactuals

Token-level attributions have been extensively studied to explain model predictions for a wide range of classification tasks in NLP (e.g., sentiment analysis), but such explanation techniques are less explored for machine reading comprehension (RC) tasks. Although the transformer-based models used here are identical to those used for classification, the underlying reasoning these models perform is very different and different types of explanations are required. We propose a methodology to evaluate explanations: an explanation should allow us to understand the RC model's high-level behavior with respect to a set of realistic counterfactual input scenarios. We define these counterfactuals for several RC settings, and by connecting explanation techniques' outputs to high-level model behavior, we can evaluate how useful different explanations really are. Our analysis suggests that pairwise explanation techniques are better suited to RC than token-level attributions, which are often unfaithful in the scenarios we consider. We additionally propose an improvement to an attention-based attribution technique, resulting in explanations which better reveal the model's behavior.