Learning the Difference that Makes a Difference with Counterfactually-Augmented Data
Table 5: Accuracy of various models for sentiment analysis trained with various datasets. Orig. denotes original, Rev. denotes revised, and Orig. - Edited denotes the original dataset where the edited spans have been removed.
['Training data', 'SVM O', 'SVM R', 'NB O', 'NB R', 'ELMo O', 'ELMo R', 'Bi-LSTM O', 'Bi-LSTM R', 'BERT O', 'BERT R']
Linear models trained on revised reviews achieve 91% accuracy on revised reviews but only 58.3% on the original test set. We see similar pattern for Bi-LSTMs where accuracy drops substantially in both directions. Interestingly, while BERT models suffer drops too, they are less pronounced, perhaps a benefit of the exposure to a larger dataset where the spurious patterns may not have held. Classifiers trained on combined datasets perform well on both, often within ≈3 pts of models trained on the same amount of data taken only from the original distribution. Thus, there may be a price to pay for breaking the reliance on spurious associations, but it may not be substantial.