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Pretrained language models, such as BERT and RoBERTa, have shown largeimprovements in the commonsense reasoning benchmark COPA. However, recent workfound that many improvements in benchmarks of natural language understandingare not due to models learning the task, but due to their increasing ability toexploit superficial cues, such as tokens that occur more often in the correctanswer than the wrong one. Are BERT's and RoBERTa's good performance on COPAalso caused by this? We find superficial cues in COPA, as well as evidence thatBERT exploits these cues. To remedy this problem, we introduce Balanced COPA,an extension of COPA that does not suffer from easy-to-exploit single tokencues. We analyze BERT's and RoBERTa's performance on original and BalancedCOPA, finding that BERT relies on superficial cues when they are present, butstill achieves comparable performance once they are made ineffective,suggesting that BERT learns the task to a certain degree when forced to. Incontrast, RoBERTa does not appear to rely on superficial cues.

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