Evaluating Explanations for Reading Comprehension with Realistic Counterfactuals

• URL: http://arxiv.org/abs/2104.04515v1
• Date: Fri, 9 Apr 2021 17:55:21 GMT
• Title: Evaluating Explanations for Reading Comprehension with Realistic Counterfactuals
• Authors: Xi Ye, Rohan Nair, Greg Durrett
• Abstract summary: We propose a methodology to evaluate explanations for machine reading comprehension tasks. An explanation should allow us to understand the RC model's high-level behavior with respect to a set of realistic counterfactual input scenarios. Our analysis suggests that pairwise explanation techniques are better suited to RC than token-level attributions.
• Score: 26.641834518599303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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