When Choosing Plausible Alternatives, Clever Hans can be Clever
Table 4: Model performance on the COPA test set (Overall), on Easy instances with superficial cues, and on Hard instances without superficial cues. p-values according to Approximate Randomization Tests Noreen (1989), with ∗ indicating a significant difference between performance on Easy and Hard p<5%. Methods are pointwise mutual information (PMI), word frequency provided by the wordfreq package Speer et al. (2018), pretrained language model (LM), and next-sentence prediction (NSP).
['Model', 'Method', 'Training Data', 'Overall', 'Easy', 'Hard', 'p-value (%)']
We then compare BERT and RoBERTa with previous models on the Easy and Hard subsets.7 As Table 4 shows, previous models perform similarly on both subsets, with the exception of Sasaki et al. (2017).8 Overall both BERT (76.5%) and [CONTINUE] RoBERTa (87.7%) considerably outperform the best previous model (71.4%). However, BERT's improvements over previous work can be almost entirely attributed to high accuracy on the Easy subset: on this subset, finetuned BERT-large improves 8.6 percent over the model by (Sasaki et al., 2017) (83.9% vs. 75.3%), but on the Hard subset, the improvement is only 2.9 percent (71.9% vs. 69.0%). This indicates that BERT relies on superficial cues. The difference between accuracy on Easy and Hard is less pronounced for RoBERTa, but still suggests some reliance on superficial cues.