When Choosing Plausible Alternatives, Clever Hans can be Clever

By Pride Kavumba, Naoya Inoue, Benjamin Heinzerling, Keshav Singh, Paul Reisert, Kentaro Inui. 2019

63 citations recorded by the source corpus.

Pretrained language models, such as BERT and RoBERTa, have shown large improvements in the commonsense reasoning benchmark COPA. However, recent work found that many improvements in benchmarks of natural language understanding are not due to models learning the task, but due to their increasing ability to exploit superficial cues, such as tokens that occur more often in the correct answer than the wrong one. Are BERT’s and RoBERTa’s good performance on COPA also caused by this? We find superficial cues in COPA, as well as evidence that BERT exploits these cues.To remedy this problem, we introduce Balanced COPA, an extension of COPA that does not suffer from easy-to-exploit single token cues. We analyze BERT’s and RoBERTa’s performance on original and Balanced COPA, finding that BERT relies on superficial cues when they are present, but still achieves comparable performance once they are made ineffective, suggesting that BERT learns the task to a certain degree when forced to. In contrast, RoBERTa does not appear to rely on superficial cues.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

Shortcut learning in deep neural networks (2020, background): “Feature combination crucially depends on shortcut features like word length [59, 60, 14, 61], and consequently leads to a severe lack of robustness such as an inability to generalise to more challenging test conditions [62, 63, 64, 65].”
Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models (2020, background): “…et al., 2011) dataset, where the task is to ﬁnish a given passage (similar to ROC Stories), and ARCT (Habernal et al., 2018) where the task is to select whether a statement warrants a claim, contain words that correlate with the expected prediction (Kavumba et al., 2019; Niven and Kao, 2019).”