Don’t Take the Easy Way Out:Ensemble Based Methods for Avoiding Known Dataset Biases
Table 5: F1 scores on Adversarial SQuAD and the standard SQuAD dev set using two different bias-only models.
['Debiasing Method', 'TF-IDF Filtered AddSent', 'TF-IDF Filtered AddSentOne', 'TF-IDF Filtered Dev', 'TF-IDF AddSent', 'TF-IDF AddSentOne', 'TF-IDF Dev']
Results: We find the bias product method improves performance by up to 3 points, and the learned-mixin +H model achieves up to a 9 point gain. The importance of including the entropy penalty is explained by the fact that, without the penalty, the model learns to ignore the bias by settings g(xi) close to zero. For example, on the AddSent dataset with the TF-IDF filtered bias, the learned-mixin ensemble sets g(xi) to an average of 0.13, while the learned-mixin +H ensemble increases that to 5.16. The high values are likely caused by the fact the bias-only model is very weak, since it assigns the same score to each token in each sentence, so the model can often scale it by large values. As expected, we get better results using the TF-IDF Filtered bias which is more closely tailored to how the test set was constructed.