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Shortcut reasoning is an irrational process of inference, which degrades therobustness of an NLP model. While a number of previous work has tackled theidentification of shortcut reasoning, there are still two major limitations:(i) a method for quantifying the severity of the discovered shortcut reasoningis not provided; (ii) certain types of shortcut reasoning may be missed. Toaddress these issues, we propose a novel method for identifying shortcutreasoning. The proposed method quantifies the severity of the shortcutreasoning by leveraging out-of-distribution data and does not make anyassumptions about the type of tokens triggering the shortcut reasoning. Ourexperiments on Natural Language Inference and Sentiment Analysis demonstratethat our framework successfully discovers known and unknown shortcut reasoningin the previous work.

Discovering Highly Influential Shortcut Reasoning: An Automated Template-Free Approach